Fragment Based Drug Design: From Experimental to Computational

Current Medicinal Chemistry, 2012, 19, ????-???? 1

0929-8673/12 $58.00+.00 © 2012 Bentham Science Publishers

Fragment Based Drug Design: From Experimental to Computational Approaches

A. Kumar, A. Voet and K.Y.J. Zhang*

Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

Abstract: Fragment based drug design has emerged as an effective alternative to high throughput screening for the identification of lead compounds in drug discovery in the past fifteen years. Fragment based screening and optimization methods have achieved credible success in many drug discovery projects with one approved drug and many more compounds in clinical trials. The fragment based drug design starts with the identification of fragments or low molecular weight compounds that generally bind with weak affinity to the target of interest. The fragments that form high quality interactions are then optimized to lead compounds with high affinity and selectivity. The weak affinity of fragments for their target requires the use of biophysical techniques such as nuclear magnetic resonance, X-ray crystallography or surface plasmon resonance to identify hits. These techniques are very sensitive and some of them provide detailed protein fragment interaction information that is important for fragment to lead optimization. Despite the huge advances in technology in the past years, experimental methods of fragment screening suffer several challenges such as low throughput, high cost of instruments and experiments, high protein and fragment concentration requirements. To address challenges posed by experimental screening approaches, computational methods were developed that play an important role in fragment library design, fragment screening and optimization of initial fragment hits. The computational approaches of fragment screening and optimization are most useful when they are used in combination with experimental approaches. The use of virtual fragment based screening in combination with experimental methods has fostered the application of fragment based drug design to important biological targets including protein-protein interactions and membrane proteins such as GPCRs. This review provides an overview of experimental and computational screening approaches used in fragment based drug discovery with an emphasis on recent successes achieved in discovering potent lead molecules using these approaches.

Keywords: Computational fragment based drug design, de novo design, fragment based drug design, fragment growing, fragment linking, ligand efficiency, molecular docking, scaffold based drug design, protein-protein interactions, small molecule protein-protein interaction inhibitors.

1. INTRODUCTION

Drug discovery is a highly interdisciplinary endeavor that involves a multitude of specialty areas and can be characterized in multiple steps. Generally, it starts with target identification and validation, followed by lead identification and optimization, then progressing to preclinical studies on animals and ends up with clinical trials in humans. This review only covers the lead identification and optimization aspect of drug discovery, specifically focusing on a relatively new technique of fragment-based drug design covering both experimental and computational approaches. The identification and optimization of lead compounds is critical in the drug discovery process and plentitude of methods, such as high-throughput screening (HTS)[1], QSAR[2], structure-based drug design [3], combinatorial chemistry [4-6], high content screening [7-9] have been developed to identify novel and potent chemical compounds against biological targets and to optimize them into leads. The hits identified from HTS screens of large corporate compound collections especially those of combinatorial chemistry origin tend to be large albeit potent. The chemical optimization of those compounds has led to some high profile failures of lead series. These have been attributed to the reduced productivity of pharmaceutical industry [10]. Partially motivated by searching for an answer to this question, the Lipinski’s “Rule of five” was proposed that have highlighted some important properties that good lead compounds should possess [11]. One of the factors identified is the correlation of high molecular weight (MW) with poor solubility. If one starts with very potent but high molecular weight lead compounds, optimization may result in molecules with even higher molecular weight with reduced solubility and this is generally associated with poor pharmacokinetic (PK) properties. To address this problem, a fragment-based drug design (FBDD) approach was proposed [12]. In the past fifteen years, FBDD has become an established strategy to discover novel chemical entities in both industry and academia [13]. The FBDD approach represents

*Address correspondence to this author at the Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Tel: +81-48-467-8792; Fax: +81-48-467-8790; E-mail: [email protected]

a rapid, resource efficient and productive route to the identification of novel hits in the early phase of drug discovery process. This method was proposed by Fesik and co-workers in 1996 at Abbott Laboratories [12]. On a historical note, the FBDD concept was also proposed earlier in 1990 by Hol and co-workers [14]. FBDD approach focuses on the identification of compounds low in molecular-weight and chemical complexity, which target sub-pockets within the target binding site. These fragment hits are expected to be more suitable starting points for “hit to lead optimization” due to their reduced complexity, which leaves more freedom for multidimensional property optimization of the fragment hits. The optimization of fragments is an iterative process where the potency of the initial fragment is improved in each step by adding functional groups, or linking two independent fragments together. FBDD is hallmarked by three advantages compared with a conventional HTS drug discovery approach [10, 15]. The first advantage is that the chemical diversity space is better covered with FBDD. In FBDD, smaller fragment libraries are required to probe chemical space more effectively while generating the same amount of information as generated by screening a huge number of compounds. A theoretical analysis by Reymond and coworkers [16, 17] suggests that each fragment represents enormous number of bigger compounds. Their analysis suggests that each additional heavy atom added to a molecule increases its chemical space by approximately eight folds. Also, Roughley and Hubbard [18] analyzed this theory in the real world and found that the chemical space is indeed more efficiently sampled with fragments than with larger molecules in the case of Hsp90. The second advantage is that screening a fragment library achieves higher hit rates as compared to conventional HTS. This may be attributed to the fact that a fragment molecule can bind to various subsites of a target in many ways. Large molecules, on the other hand, contain more functional groups that may present more steric hindrance or electrostatic clashes than the fragment molecule in a binding site. These molecular incompatibilities prevent most large molecules from being accommodated in the protein pockets [15, 19]. Finally, the third advantage is that compounds optimized from fragments exhibit high binding efficiency per atom as compared to compounds optimized from HTS. FBDD has now evolved into a very successful drug discovery strategy since its conception in 1996

wasim

Final

2 Current Medicinal Chemistry, 2012 Vol. 19, No. 1 Kumar et al.

[12]. There is already an FDA approved drug [20] and at least 10 more compounds in clinical trials which originate from fragment screening and optimization approaches [21]. In recent years, FBDD has gained an important place as a screening tool for novel inhibitors. This is reflected in the number of publications on FBDD each year. In the year 2011 alone over 125 papers (of which 17 are reviews) were published. Furthermore in the last decade at least 5 books were published dealing with methods and successes in FBDD [19, 22-25]. There are two very good blogs dedicated to discussion about FBDD highlighting its importance [http:// practicalfragments.blogspot.com/ and http://fbdd-lit.blogspot.com/]. A recent review by Hajduk and coworkers [26] listed 19 pharmaceutical companies that are using FBDD in their drug discovery efforts. Another list compiled on http:// practicalfragments.blogspot.com/ blog included 44 companies involved in FBDD related projects.

Realizing the significance of FBDD in drug discovery, many approaches were developed that either complement FBDD or improve FBDD to realize its full potential. In order to overcome its challenge of not being able to screen fragments with biochemical assays, also motivated by the search for chemical scaffolds that are well-known reoccurring motifs in marketed drugs, a scaffold-based drug design (SBDD) approach was proposed [27]. Instead of using basic chemical building blocks (MW < 150 Da) as fragments in classical FBDD, the SBDD method uses chemical scaffolds that are significantly larger (MW 125 - 350 Da). These larger scaffold-like compounds are richer in functional groups that could form key interactions with the target protein - thus providing a more robust anchoring point for subsequent chemical optimization through substitution. There are several significant differences between classical FBDD and SBDD. First, the compounds used for SBDD are significantly larger with an average molecular weight of about 250 Da. Consequently, the size of the scaffold library is bigger with about 20,000 compounds instead of 100-2000 for FBDD. Secondly, these scaffold-like compounds could bind to target protein at low affinity and are detectable by biochemical assays. To reduce the high false positive rate associated with high compound concentration and low binding affinity, only compounds that show activity against multiple members in the same protein family are selected as hits. Thirdly, biophysical methods such as X-ray crystallography are used as a secondary filter instead of primary screen as in FBDD. Finally, only scaffolds with binding modes that are tolerant to small substitutions are selected for further optimization. This leads to more predictable SAR and more efficient chemical optimization.

Complimentary to the classical experimental screening approach, is the computational fragment based drug design. Similar to the classical rational drug design strategies, computational tools are successfully used in different steps of FBDD from fragment library design to identification and optimization of fragment hits. Although computational fragment screening is in preliminary stage and yet to realize its full potential but this approach has shown early promises to become a widely used tool to complement experimental methods of fragment screening.

2. FRAGMENT BASED DRUG DESIGN: DEFINITION AND

CONCEPT

2.1. Fragment Definition

Fragments are defined as low molecular weight, moderately lipophilic, highly soluble organic molecules. Fragments typically bind to their target protein with low affinity, generally in the μM to mM range, and can be grown, merged or linked with another fragment to improve the potency. In analogy to Lipinski’s “Rule of five” [11], a “Rule of three” was proposed to define fragments by Congreve et al.[28]. This rule was derived after carefully analyzing

hits from various fragment screens. The “Rule of three” states that fragments should have a molecular weight < 300 Da, cLogP 3, number of hydrogen bond donors 3 and number of hydrogen bond acceptors 3. Their analysis also indicates that using additional filters, such as the number of rotatable bonds 3 and the polar surface area (PSA) 60 Å2, would give more desirable fragment-like compounds. Although the “Rule of three” is widely accepted, the guidelines vary according to the different interpretations by different research teams. Most of these modifications to the rule are particularly related with molecular weight. For example, researchers at Plexxikon screened a library of scaffold-like compounds with molecular weight ranges from 125 Da to 350 Da to identify hits for variety of drug targets including PPARs, PDE-4 and BRAFV600E [27, 29-31].

2.2. Ligand Efficiency and Other Efficiency Indices

Fragments generally have a weak binding affinity for their protein targets; thus typically the affinity needs to be optimized by adding new functional groups or by linking two hit fragments bound in adjacent pockets. An important question that needs to be addressed here is “how to select the best fragment for optimization?” as there are many fragment hits to start with. Ligand efficiency (LE) [32, 33] is a widely used concept and is generally used for comparing different hit fragments to guide the lead generation and optimization process. LE was proposed by Hopkins et al. [32] based on the concept of the “Andrew binding energy”[34] and the practice of using experimental binding affinity [33]. Ligand efficiency is the free energy of binding divided by the number of heavy (non-hydrogen) atoms and is defined by the following equation:

HAC

ICRT

HAC

KRT

HAC

GLE

d)ln()ln(

50==

which G is the binding free energy, R is the gas constant, T is the absolute temperature, Kd, is the dissociation binding constant, IC50 is the concentration of inhibitor required to inhibit 50% activity of the enzyme, and HAC is the number of heavy atoms. The LE is considered a very important parameter in FBDD. A fragment with a high LE is preferred for optimization because it provides an opportunity to obtain highly active compounds, without increasing the molecular weight too much. The commonly used lower acceptable threshold value for LE is 0.3 kcal/mol per heavy atom, which is derived from a hypothetical drug molecule with a high Kd value of 10nM and a molecular weight of 500 Da (the upper molecular weight limit according to Lipinski’s rule) and contains approximately 36 heavy atoms. A retrospective analysis of highly optimized inhibitors by Hajduk et al. [35] also indicated a linear relationship between the molecular weight and the potency during optimization of fragment hits. With each heavy atom added to the initial fragment, the binding energy is increased by 0.3 kcal/mol. Therefore, fragments with high LE are preferred over the fragments with lower LE for optimization.

Although LE is a widely used metric, it also has some drawbacks. One of them is that it does not display similar behavior with ligands of different sizes. LE works very well with smaller compounds, but is relatively insensitive to compounds with a high molecular weight. To overcome the shortcomings associated with LE, a number of other metrics have been proposed by various groups in recent years for evaluating fragment quality. Some of these metrics are listed in (Table 1) along with their definitions. These include closely related metrics, such as percent efficiency index (PEI) and binding efficiency index (BEI), developed by researchers at the Abbott Laboratories [36]. The PEI is the percentage of inhibition at a given concentration of compound, divided by the molecular weight. The BEI is the negative logarithm of the inhibition constant divided by the molecular weight. The

Fragment Based Drug Design Current Medicinal Chemistry, 2012 Vol. 19, No. 1 3

Table 1. Ligand Efficiency Indices

Name Abbreviation Definition Reference

Ligand Efficiency LE HAC

ICRT

HAC

KRT

HAC

GLE

d)ln()ln(

50==

Hopkins et al. (2004)

Percent Efficiency Index PEI MW

compoundofionconcentratgivenaatinhibition% Abad-Zapatero and Metz (2005)

Binding Efficiency Index BEI MW

pKorpKdi)( Abad-Zapatero and Metz

(2005)

Surface Binding Efficiency Index SEI PSA

pKorpKdi)( Abad-Zapatero and Metz

(2005)

Fit Quality Score FQ

,

))(

47222.361

)(

7079.25

)(

538.70715.0(

32HAHAHA

LE

+++

Reynolds et al. (2007)

Percent Ligand Efficiency %LE

,

)(618.1

100)

10(2log

ratiogoldenwhere

LE

HA

=

Orita et al. (2009)

Ligand Lipophilicity Efficiency LLE PcICorpKi

log)(50

Leeson and Springthorpe (2007)

Ligand Lipophilicity Efficiency at Astex Therapeutics

LLEAT )ln()ln(

11.0

50

*

*

PRTICRTGGGwhere

HA

G

lipo=

Mortenson and Murray (2011)

Ligand-Efficiency-Dependent Lipophilicity

LELP LE

Plog Keseru and Makara (2009)

Group Efficiency GE

BmoleculeformtoAmoleculeatoaddedgroupfunctionalawhen

AHABHAHAandAGBGGwhere

HAG

ab)()()()( ==

= Verdonk and Rees (2008)

Kinetic Efficiency KE

,

ondissociatiforlifehalfistandstantconrelaxationiswhere

HA

t

HA

21

=693.0

21

Holdgate and Gill (2011)

surface-binding efficiency index (SEI), calculated by dividing the pKi by the polar surface area (PSA) was also proposed as an FBDD metric [36]. Some metrics, such as the fit quality score (FQ)[37] and percent ligand efficiency (%LE)[38], are size independent and allow the comparison of various fragments irrespective to their molecular weight. To address the challenges of high lipophilicity, which is one of the main causes of increased attrition rate in the clinical trials, Leeson and Springthorpe proposed the ligand lipophilicity efficiency (LLE) index, which subtracts logP from –log10IC50 [39]. Mortenson and Murray [40] from Astex Therapeutics proposed a similar metric Ligand Lipophilicity Efficiency AT Astex Therapeutics (LLEAT) to account for molecule size along with lipophilicity. A related metric proposed by Keseru and Makara [41] is Ligand-Efficiency-Dependent Lipophilicity (LELP), which is simply logP/LE. Verdonk and Rees [42] proposed the concept of Group Efficiency (GE) that estimates the binding efficiency of groups added to an existing lead. GE is considered analogous to LE where the change in binding energy is divided by the change in the number of heavy atoms. Recently, Holdgate and Gill [43] from AstraZeneca proposed the Kinetic Efficiency (KE) that is a metric to address the kinetics of ligand binding to a protein. The KE is devised to complement the LE and other metrics and is suitable for later stages of fragment optimization. However, KE usage is limited to the earlier stages because of the rapid kinetics involved with small low molecular weight fragments.

3. METHODS FOR FRAGMENT SCREENING AND

OPTIMIZATION

The process of FBDD consists of two steps: (a) the identification of the initial fragment hit with a weak binding affinity and (b) the optimization of the fragment hit into a high affinity lead compound.

3.1. Identification of Fragment Hit

The first step in FBDD is the identification of the initial fragment hit that has sufficiently high LE and that can be used as an anchor for the development of large and potent lead compounds. Fragments are smaller in size, making few interactions with the protein and displaying low binding affinity. This makes them particularly difficult to be detected by standard biochemical assays. Instead, biophysical methods such as nuclear magnetic resonance (NMR) and X-ray crystallography were commonly used to identify these low molecular weight compounds. In the past decade, there has been a considerable effort in the development of screening technologies for fragment detection and many new technologies were developed including native mass spectrometry, isothermal titration calorimetry (ITC), surface plasmon resonance (SPR), capillary electrophoresis, weak affinity chromatography, biolayer interferometry and ultra-filteration. The development efforts were


focused on two types of fragment screening methods: Methods that detect binding of fragments and methods that reveal binding interactions. Most of the fragment screening technologies like ligand-detected NMR, SPR, ITC etc. belong to the former group and offer various degrees of binding affinity information. X-ray crystallography and protein-detected NMR are the only two fragment screening methodologies that give protein ligand interaction information. The following paragraphs give an overview of these screening technologies. However, for a more thorough understanding of these fragment screening methodologies, the reader is referred to other in-depth reviews describing the advantages and disadvantages, which have been published in recent years [15, 21, 44-46].

3.1.1. Nuclear Magnetic Resonance (NMR) Spectroscopy

NMR spectroscopy is a method that exploits the magnetic properties of certain atomic nuclei to provide detailed information about the structure, dynamics, reaction state, and chemical environment of molecules in which they are contained. NMR spectroscopy is widely used in fragment screening and was first described by Fesik and co-workers at Abbott Laboratories in their work “SAR by NMR”[12], where they first demonstrated the feasibility and first practical success of FBDD. In their approach, they observed changes in protein amide chemical shifts obtained from 2D-NMR spectra (specifically 1H/15N HSQC) in the presence and absence of fragments. This screening resulted in two non-competitive hits, which were first individually optimized into moderate affinity binders with a Kd value of 2 and 100 M. In the last step, a high affinity binder with a Kd value of 19 nM was obtained by linking the two moderate affinity binders. This type of fragment screening is known as protein-detected NMR and is one of the two NMR-based fragment screening approaches. The other NMR-based fragment screening approach is ligand-detected NMR in which changes in NMR properties (such as Nuclear Overhouser Effect (NOE), relaxation rates, and magnetization transfer) of fragment are detected instead of the target protein. The protein-detected NMR can detect nM to mM interactions and yields precise information about the binding site and protein ligand interaction. This approach however is restricted to smaller proteins (< 50kDa) and requires large quantities (about 50-200mg) of isotopically labeled protein with a high solubility [47, 48]. To overcome the challenges associated with protein-detected NMR, ligand-detected NMR methods were developed by several groups that rely on changes in fragment signals while binding to the target protein [48]. Ligand-detected NMR methods do not provide information about the ligand binding site and additional experiments need to be performed to obtain this information. The most commonly used ligand-detected NMR technologies are Saturation Transfer Difference-NMR (STD-NMR)[49, 50] and Water Ligand Optimized Gradient Spectroscopy (Water-LOGSY)[51, 52]. Both of these methods measure the 1H NMR signals of fragments that differ in their relaxation properties, signs and intensity for bound and unbound fragments. In STD-NMR, the target protein becomes saturated after the excitation of selective hydrogens. Due to spin diffusion the saturation can spread over the hydrogens across target protein and onto the bound fragments, whereas unbound fragments remain unaffected. The 1H NMR difference spectrum is then recorded between on- and off-resonance excitation of the protein which results in positive signals for bound fragments. Water-LOGSY is one of most sensitive ligand-detected NMR technique that is based on the theory that binding of fragment displaces water from the binding site. Water-LOGSY exploits the intermolecular magnetization transfer from bulk water to the protein binding site and onto the bound ligands. Other reported ligand-detected NMR methods include FAXS (Fluorine chemical shift Anisotropy and eXchange for Screening) method [53] and TINS (Target Immobilized NMR Screening) method [54, 55]. FAXS is 19F screening technique based on 19F detection and allows the

determination of Kd and IC50 of the identified binding ligands. A recent paper by Jordan et al. [56] demonstrates the practical application of FAXS in FBDD. In the TINS fragment screen method, a mixture of fragments is passed through a resin with immobilized target protein and a reference in an automated process. A 1D 1H NMR spectrum is recorded for each mixture of fragments while the fragment binding is determined by measuring the reduction in their NMR amplitudes. The reference serves to cancel out the non-specific binding of fragments to protein surfaces. NMR based fragment screening is now routinely used in various FBDD campaigns within the pharmaceutical companies like Abbott Laboratories, Merck, Vernalis, Astex Therapeutics and Evotec and many academic laboratories.

3.1.2. X-ray Crystallography

X-ray crystallography is a commonly used method in structural biology. It can be applied to very large proteins, as well as other biopolymers, and provides very high-resolution structural data. Compounds with affinity for a pocket can be soaked into the crystallized protein and after diffraction the compound will be visible in the pocket as an electron density cloud. It has become one of the preferred methods for common structure based drug discovery efforts [57, 58].

X-ray crystallography however can also play an important role in the identification of hit fragments. During a crystallographic screening experiment, the investigated fragments will be soaked into the crystal. To speed up the process and reduce the costs, the screened fragments are usually pooled into cocktails containing (usually 10) different fragments. After solving the crystal structures of the receptor protein, one can easily identify bound fragments in the protein pockets [59].

There are several companies who used the cocktail-soaking method in their drug discovery programs. Astex Therapeutics identified fragments for the cyclin dependent kinase (CDK) 2 that resulted in the design of AT7519 [60], as well as fragments that evolved into the AT9283 auroraA kinase inhibitor [61]. Both of them are currently in clinical trials for cancer therapy. Furthermore the same company used this method to identify a urokinase inhibitor which exhibits a promising PK profile [62]. SGX (Eli Lilly) reported allosteric inhibitors for the Hepatitis C virus NS5b RNA polymerase [63], as well as the design of a JAK2 over JAK3 specific kinase inhibitor, after X-ray crystallography based screening and structure based optimization of the fragments [64]. Researchers at deCODE Biostructures Inc. developed DG-051, a leukotriene A4 hydrolase inhibitor, currently in Phase 2 clinical trials, after screening 1300 fragments using this crystallographic method [65].

While previous results clearly indicate the applicability of X-ray based fragment screening methods, there are some drawbacks associated with it. It is essential to be able to make a crystal of the target protein, consequently protein targets that haven’t been crystallized are excluded from this procedure. Furthermore X-ray based crystallography cannot be used to determine the affinity of the bound fragments, for this purpose a secondary method should be employed. The major advantage however is the presence of a high resolution image of the complex, which can be directly used for structure based drug discovery efforts [58].

X-ray crystallography also plays an important role during the optimization phases of fragments (see section 3.2). Once the crystal structures with the fragments have been solved, the structural insights can be employed to evolve the fragments into potent drug-like molecules. By exploiting the structural insights of the fragment bound to the receptor, fragments can be grown into larger molecules. Also linking simultaneously bound fragments can be facilitated by including the bound fragment geometry during the design of a linked molecule.


3.1.3. Surface Plasmon Resonance

SPR is a generally used technique to study biomolecular interactions revealing both kinetic as well as binding affinity information. SPR is a highly sensitive technique and is becoming increasingly popular as a fast and cost effective primary screening technology to identify fragment hits. In most cases of SPR-based experiments, the target protein is immobilized on the chip (generally a gold coated glass slide) and fragments are passed through it. The binding of molecules to the target protein, which is immobilized on the gold layer, causes a change in the absorbance spectrum of the reflected light related to a change in the medium absorbed on the gold layer. These changes are related to the mass of fragment and protein and are efficiently measured by an SPR instrument [66]. Initial SPR based biosensors were not sensitive enough to measure interactions of small molecules and were limited to only macromolecules. But with recent technical advances, SPR biosensors are capable of detecting weak binding of fragments to the target protein and are suitable for fragment screening [67]. SPR was successfully used in various fragment screening projects to identify potent fragment hits against a number of protein targets including BACE-1 [68-70], Pim-1 [71], MMP-12 [72], HIV-1 reverse transcriptase [73], HIV-1 protease [74], carbonic anhydrase II [74, 75], human serum albumin [74], thrombin [74], chymase [76] and CCR5 [77]. In one of these screening projects, Xiang et al. [71] used SPR followed by a biochemical assay to screen a library of 1800 fragments at 75 μM concentration in order to identify Pim-1 inhibitors. One fragment hit with a benzofuran core that initially displayed IC50 of 8.5 μM was optimized to 1nM by adding an aminocyclohexanylamino group at the 7-position of the benzofuran core. SPR has been also used by de Kloe et al. [78] to identify hotspots regions for small molecule binding in the protein. The researchers deconstructed potent ligands for nicotinic acetylcholine binding protein (AChBP) containing quinuclidine core into 20 fragments. The binding of these fragments was evaluated by an SPR biosensor assay and revealed LE hotspots regions that can be used to identify promising hits in a fragment screening campaign.

3.1.4. Biolayer Interferometry

Biolayer interferometry (BLI) is a recent technique which measures changes in the interference pattern of light between the sensor and the solution, caused by fragment binding to an immobilized target protein on the surface of the sensor. In a recent paper by Wartchow et al. [79] its fragment screening capabilities are demonstrated for three proteins (Bcl-2, JNK1, and eIF4E) and compared with those of other fragment screening methods.

3.1.5. Isothermal Titration Calorimetry

ITC is a thermodynamic technique that measure the heat released or absorbed during a biomolecular binding event. ITC allows the accurate determination of thermodynamic properties like binding constants (KB), reaction stoichiometry (n), enthalpy ( H) and entropy ( S) in a single experiment [80, 81]. ITC is used by some researchers as a fragment screening tool and it measures the heat released when a fragment binds to a protein. Although ITC is a powerful screening tool, it is low throughput in nature and requires higher protein concentration than other screening approaches. Though with the use of recent technology like the AutoITC200 (http://www.microcal.com) 50-100 samples can be processed in a day but still ITC is better suited for secondary screenings rather than primary fragment screening.

3.1.6. Mass Spectrometry

Mass spectrometry is an analytical technique in which the gaseous ionic state is studied by transferring the analytes from the condensed phase to the gas phase followed by their ionization. Mass spectrometry then measures the mass-to-charge ratio in gas phase ionized molecule to detect its molecular weight. These days,

mass spectrometry can be also used to effectively detect fragments binding to protein as it is high-throughput in nature and consumes little amount of sample. Moreover, fragment mixtures can be used and the stoichiometric information and dissociation constants can be determined. There are two types of approaches: (a) Electrospray ionization mass spectrometry (ESI-MS)[82] detects the covalently bound fragments; (b) Non-covalent electrospray ionization mass spectrometry (NC-ESI-MS), or simply native mass spectrometry [83], can detect fragments that bind non-covalently to the target protein with Kd upto mM range. A French company NovAliX (http://www.novalix-pharma.com) has demonstrated the practical application of NC-ESI-MS. They screened a fragment library of about 350 compounds against Hsp90 which resulted in 40 fragments binding to Hsp90 [83].

3.1.7. Weak Affinity Chromatography

Duong-Thi et al. [84] recently proposed weak affinity chromatography (WAC) as an alternative to other fragment screening methods. They used WAC to screen fragments against trypsin and thrombin. In WAC, the screened fragments are passed through a chromatography column with immobilized target protein. Fragments having affinity for protein stay in the columns and are later detected with either UV spectrometry or mass spectrometry. The current implementation of this technique allows the detection of fragments in the 1mM to 10μM range, with very low consumption of fragments and protein. Although WAC is a new technique and needs to be developed further for more diverse targets and larger fragment libraries, its simplicity, reproducible and adaptable nature defines it as a suited complementary fragment screening technique.

3.1.8. Capillary Electrophoresis

Capillary electrophoresis was initially developed for HTS [85, 86] and has led to the discovery of multiple lead compounds. Capillary electrophoresis was modified for the screening of fragments (CEfragTM) by Selcia, a drug discovery screening service company. In this approach, fragments are detected by monitoring the changes in the electrophoretic profile of fragments displaying affinities in the mM to pM range.

3.1.9. Ultrafiltration

Ultrafiltration is similar to capillary electrophoresis and weak affinity chromatography as all these methods involve affinity based separation of bound and unbound fragments. The principle of ultrafiltration is also simple in which fragments to be screened are mixed with protein and passed through a membrane which retains the protein (complexed with bound fragments). The composition of the initial mixture is then compared with the filtrate composition to identify the fragments that have affinity for the protein. This fragment screening technique was tested on two proteins [87]: riboflavin kinase and methionine aminopeptidase 1. The screening resulted into 3 and 9 fragment hits for riboflavin kinase and methionine aminopeptidase 1 respectively.

3.1.10. Biochemical Assays/High Concentration Screenings

Standard biochemical assays are not the preferred method in FBDD due to their inability to detect fragments with a very weak binding affinity for the protein target. The higher concentration of fragments used during screening causes higher number of false positives and negatives due to aggregation, chemical reactivity and interference with the assay. Moreover, high concentration screening requires high solubility (about 1mM) of the fragments. Despite the disadvantages with the use of biochemical assays, recent studies showed that good fragment hits can be identified by screening fragment library at a higher concentration effectively, rapidly and cheaply. The higher number of false positives and negatives can be avoided by removing fragments with poor solubility, reactive fragments, frequent hitters and aggregators from the fragment library. Plexxikon, for example, has routinely used high-


concentration biochemical screening to look for inhibitors or activators of enzyme activity. They have screened scaffold-like compound libraries at a concentration ranging from 100 μM to 200μM using different functional assays. X-ray crystallography was used as a secondary screen to further prioritize hits after high concentration screening. Plexxikon’s effort ultimately led to the identification of PLX4032 (Vemurafenib) (see section 4). Other groups also used high concentration biochemical screening as their primary fragment screening approach to identify inhibitors of phosphatidylinositol-3 kinase [88], phosphoinositide-dependent kinase-1 (PDK1)[89] and beta-secretase (BACE-1)[90]. Nowadays, with significant successes of using high concentration screening by biological assays reported, many groups in pharmaceutical industry are using fragments in HTS screening at higher concentration to develop target focused sets of fragments for further biophysical screening [15].

3.1.11. Tethering

Tethering or covalent tethering is a fragment based screening approach developed at Sunesis Pharmaceuticals and it allows the detection of very weakly binding fragments that cannot be detected by traditional means. Tethering uses the reversible disulfide bonds between cysteine residues in a protein and thiol linker containing fragments to capture and identify weakly binding fragments by mass spectrometry [91-93]. In tethering approach, single cysteine mutations are introduced in the target protein surface at the site of interest. A library of thiol linker containing fragments is then screened against this cysteine mutant protein. The thiol linker containing fragments compete for disulfide formation with the cysteine residue introduced in the protein. The fragments that have inherent affinity for the protein apart from disulfide bond are conjugated at equilibrium and are detected by mass spectrometry. Tethering approach has been used to identify inhibitor of interleukin-2 [94], caspase-3 [95, 96], GPCR [97] etc. Different versions of tethering also exist such as extended tethering and tethering with dynamic extenders. Extended tethering or tethering with extenders is a modification to covalent tethering by combining with aspects from dynamic combinatorial chemistry [96]. In extended tethering, cysteine residue is covalently linked using irreversible electrophile of ‘extender’ which is a protected thiol group containing small molecule that has some inherent affinity for the protein. The thiol group is deprotected to screen a library of disulfide containing fragments. Fragments making favorable interactions with protein will form stable disulfide bonds with extender and are detected by mass spectrometry. Recently, Erlanson et al. [98] used tethering with extender to identify inhibitors of 3-phosphoinositide dependent protein kinase-1 (PDK1). Another modification to tethering is tethering with dynamic extenders, in which an irreversible electrophile of ‘extender’ is replaced with disulfide that enables reversible cysteine modification. Cancilla et al. [99] used tethering with dynamic extenders to discover an Aurora kinase inhibitor.

3.1.12. Computational Methods

Computational methods, such as molecular docking, have also been used alone or in combination with experimental fragment screening approaches to successfully identify fragment hits for optimization into lead-like compounds. The detailed discussion about these methods and practical applications is presented in Section 6 of this review.

3.2. Fragment Optimization

There are two commonly used approaches for the optimization of fragment hits into lead-like compounds: (a) Fragment growing and (b) Fragment linking. Fragment growing is the stepwise addition of functional groups or substituents to the fragment core to maximize the favorable interactions with the binding site residues. The fragment linking approach is based on covalently linking two

or more fragments bound independently in proximity with suitable linkers. A schematic overview of fragment growing and fragment linking is presented in Fig. (1) using the data derived from the study by Hung et al. [100] Both approaches are described in detail with examples in following paragraphs.

3.2.1. Fragment Growing

Fragment growing is the most common and popular approach for the optimization of fragment hits into lead-like compounds [13]. Fragment growing is an iterative process and at each step additional features are added to the fragments core with the goal of improving potency and pharmacological properties Fig. (1a). The structural information derived from X-ray crystallography or NMR is generally used to guide the substitution or addition of functional groups with the ultimate goal of improving potency. The most important consideration while growing from an initial fragment is that it conserves the binding mode of initial fragment in the optimized compound Fig. (1a). One of the great advantages with the use of fragment growing is that subtle changes in binding mode with each step of the fragment optimization can be monitored [13]. A large number of successful examples of using growing approach for fragment optimization have been reported in literature. We cannot cover all of these studies and a number of recently published reviews can be consulted [10, 13, 15, 21, 101, 102]. Recent studies that utilized fragment growing as an optimization strategy include the discovery of Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1)[70], Acetylcholine-binding protein (AChBP)[103], Matrix metalloproteinases (MMPs)[104] and phosphatidylinositol-3 kinases (PI3Ks)[88] inhibitors. In one of these studies, Cheng et al. [70] screened a library of 4000 fragments against BACE1, using SPR and identified 2-aminoquinoline as initial fragment hit. This hit initially displayed a potency of 900 μM and was improved 106 fold by fragment growing based optimization to an IC50 value of 11 nM on BACE1 and cellular activity of 80 nM. Edink et al. [103] also used fragment growing to optimize a fragment by growing into ligand induced subpocket of AChBP binding site. They started with the co-crystallization and structure solution of a moderately potent fragment with AChBP. The structure revealed absence of one sub-pocket which was present in co-crystal structure of AChBP with a natural product lobeline. The fragment binds in the same way as the natural product lobeline, but it lacks the hydroxyphenetyl group, which extends to a subpocket in AChBP binding site. To optimize this fragment hit, the researchers introduced hydroxyphenetyl group on the fragment which led to a 50 fold more potent compound. The crystal structure was also obtained for this molecule which confirms successful fragment growing and the hydroxyphenetyl group indeed binds into the lobeline subpocket. The authors also described structural and thermodynamic consequences of fragment growing in their paper.

3.2.2. Fragment Linking

Fragment linking is less common than fragment growing, but linking fragments that bind in adjacent sites of target protein is a powerful fragment optimization approach to turn low affinity fragments into high affinity leads Fig. (1b). Fragment linking was first successfully demonstrated by Fesik and co-workers in their “SAR by NMR” paper [12]. Since then, the fragment linking approach has been used by a number of groups [100, 105-112] to link two weak affinity fragments to obtain highly potent compound. One of these studies describes linking two low affinity fragments identified from a fragment screen against Hsp90 [111] to obtain a compound with 1000 fold higher affinity than initial fragments against Hsp90 while maintaining LE. X-ray crystallography of linked compound also revealed that it displays similar binding mode as two initial fragments. In another study, Petros et al. [110] identified through fragment linking approach a highly potent Bcl-2 inhibitor that was 1000 fold selective for Bcl-2 over Bcl-xL. They started with a less potent and moderately selective compound


identified from protein-detected NMR screen of 17000 fragments. The researchers screened a smaller library in the presence of this less potent and moderately selective compound to identify a second fragment that can be linked and occupy an adjacent hydrophobic subsite. This study provided a very good example for improving potency and selectivity using fragment linking.

Recently, Ichihara et al. [113] reviewed successful fragment linking reports and suggested a strategy to maximize the success in fragment linking approach. They proposed that super-additivity (when binding free energy of the linked fragments is more than the sum of the binding energies of individual fragments) can be achieved by carefully selecting the fragment pair for optimization. To achieve successful fragment linking, binding mode of individual fragments need to remain conserved during optimization. But it is very difficult to obtain or synthesize such an ideal linker. In such cases, fragment pair where one fragment form strong H-bonds with binding site residues and other fragment interact via hydrophobic or van der Waals interactions (more tolerant to changes in binding mode) may be chosen. Also, Yamane et al. [114] suggested in-crystal chemical ligation for effectively linking two fragments. Their strategy involves first soaking the target protein apo-crystals with anchor molecules (in their study trypsin as target protein and benzamidine as anchor molecule) and then transferring these protein crystals into another solution of tuning molecules. The crystals are then analyzed for any bound ligand i.e. tuning molecules that can form stable ligated product with anchor molecule at binding site. Although their study did not result in any molecule that can bind with better affinity than initial fragment, superior binders however might be generated using a bigger tuning molecules library. Fragment in situ self assembly is another fragment linking approach used to optimize fragments into lead-like compounds. In this approach, the protein acts as a scaffold for the formation of highly potent compound through the reaction of two low affinity fragments (from a compound mixture) in close proximity to each other. The first application of fragment in situ self assembly in FBDD was demonstrated by Lewis et al. [115] where enzyme acetylcholinesterase assembles azide and alkyne fragments into an inhibitor of very high potency. In another example, Hu et al. [116] used fragment in situ self assembly to identify a small molecule protein-protein interaction inhibitor (SMPPII). They found that Bcl-XL serves as a template/scaffold for the amidation reaction between thio acids and sulfonyl azides to form a SMPPII. The latest example of fragment in situ self assembly for fragment optimization is from Suzuki et al. [117] where they have used this approach to identify histone deacetylase (HDAC) inhibitors. They have incubated HDAC8 with two hydroxamic-containing alkynes and 15 azide fragments with a goal to get a linked product able to inhibit the enzyme. They obtained one compound with greater inhibitory power than either of the individual fragments.

3.2.3. Fragment Growing Versus Linking

Out of the two choices for fragment optimization, the fragment growing approach is more popular and it’s a clear choice when there is an obvious place to grow. It gives more freedom to a medicinal chemist for multidimensional property optimization. Fragment linking has not received the same success as fragment growing, as this strategy is dependent upon the ability to chemically link adjacent fragments without disturbing the binding mode displayed by fragments alone. Although fragment linking provides a clear starting point for optimization, achieving a similar binding mode of the fragments in the final compound is very difficult considering the limited repertoire of linkers to tether the two fragments. Hung et al. [100] compared the two fragment optimization approaches by applying them on the same target pantothenate synthetase from Mycobacterium tuberculosis as illustrated in Fig. (1). They started with an indole fragment for “fragment growing” and an indole and a benzofuran fragment for

“fragment linking". These starting fragments were identified using a number of biophysical techniques including thermal shifts assay, WaterLOGSY NMR, ITC and X-ray crystallography. The optimization of initial fragment hits using two fragment optimization strategies resulted in similar compounds with similar potency Fig. (1).

3.2.4. SAR by Catalog

“SAR by catalog” is one of most common fragment optimization approach used by various researchers to optimize initial fragment hits. “SAR by catalog” is simply searching various commercially available chemical vendor’s library for similar compounds that can be purchased and tested. Jahnke et al. [118] carried out the screening of a library of 400 fragments for binding to farnesyl pyrophosphate synthase (FPPS) using NMR spectroscopy. To optimize 4 weakly binding hits obtained from fragment screening, they conducted similarity search in Novartis compound inventory. The similarity search resulted in 40 hits similar to weakly binding fragments. These hits were again tested by NMR spectroscopy and some of the hits were further characterized by ITC and X-ray crystallography. Their efforts led to the development of a compound with comparable potency to approved drugs that target FPPS. Researchers at Vernalis [119] also used “SAR by catalog” approach to search compound containing resorcinol substructure in their in-house database as a means to identify lead compounds against Hsp90. One of the hit compounds that initially showed IC50 of 300nM was optimized by medicinal chemistry modification into a 9nM potency compound. This compound AUY922 is now in Phase II clinical trials [120].

4. VEMURAFENIB, A RECENT SUCCESS WITH

EXPERIMENTAL FRAGMENT SCREENING

In this chapter we would like to highlight a recent success in FBDD by drawing the attention to the identification of PLX4032 (Vemurafenib) as a BRAF inhibitor, which was recently approved by the FDA and is marketed as Zelboraf [29, 31]. PLX4032 is one of the first approved drugs of which the origin can be traced back to a FBDD hit discovery. A more detailed account on the discovery of Vemurafenib is given in a recent review by Bollag [121].

In 2002, Davies et al. reported that activating mutations (V600E) in the BRAF encoding gene were present in a significant population of malignant melanoma patients [122]. This report caused several groups to embark on a drug discovery program targeting this oncogenic mutant BRAF kinase, including a research team at the Plexxikon Inc. (a member of Daiichi Sankyo group). They opted for a modified fragment based drug discovery approach, referred to as scaffold based drug discovery. In order to identify protein kinase scaffolds, a library of 20,000 compounds (of which the molecular mass ranged between 125 to 350 Da) was created. This library was screened at 200 M on a divergent set of structurally characterized kinases. Analysis of this data resulted in the selection 238 compounds, with at least 30% inhibitory activity at 200 M for three different kinases (Pim-1, p38, and CSK). In total over 100 structures were solved containing a small molecule. In particular a 7-azaindole drew the researchers’ attention since it was able to form key hydrogen bonding interactions within the active site and subsequently a set of derivatives were synthesized resulting in increased affinity. Overlapping the hit molecule with the structure of multiple kinases indicated that the compound was able to maintain the hydrogen bonding pattern with the kinase hinge while

also bearing several putative substitution sites for optimization of potency as well as specificity. Derivatives were designed based on structural information trying to grow the molecule so that it exploited key interactions to gain potency as well as specificity. The designed molecules were tested and excellent potency as well selectivity was identified for the BRAFV600E mutant. Guided by co-


Fig. (1). A schematic illustration of fragment optimization strategies using the data derived from the study by Hung et al. (2009) (a) Fragment growing: Initial fragment with low potency is optimized by stepwise addition of functional groups to obtain a compound with high potency. (b) Fragment linking: Two or more fragments bound independently in proximity are covalently linked with suitable linkers to obtain a compound with high potency while maintaining the binding mode.

crystal structures the compounds were further optimized resulting in PLX4720 with 13nM potency for the BRAFV600E mutant [29] Fig. (2).

The pharmacokinetic analysis in animal models of PLX4720 analogues led to the selection of PLX4032 (Vemurafenib), over PLX4720, for further clinical evaluation because of a more favorable PK profile [31]. Clinical trials conducted in collaboration with Roche indicated the efficacy and safety of Vemurafenib in treatment naive as well as pre-treated melanoma patients with the BRAFV600E mutation [123, 124], finally leading to the FDA approval of Vemurafenib for patients with unresectable or metastatic melanoma with BRAFV600E mutation [20, 29, 31].

Of note, researchers at Plexxikon have also used a similar screening strategy for the discovery a pan-PPAR inhibitor, indeglitazar, which has progressed to Phase II clinical trial [30] and selective PDE4 inhibitors [125]. As can be concluded from this successful FBDD case, FBDD approaches can indeed lead to approved drug molecules endorsing the FBDD method for drug discovery.

5. FRAGMENT BASED DRUG DESIGN: CHALLENGES

WITH EXPERIMENTAL APPROACHES

Identification of initial fragment hits with various experimental fragment screening methods is pivotal to any FBDD campaign. Therefore, highly sensitive fragment screening technologies including NMR based approaches [47-55], X-ray crystallography

[59], surface plasmon resonance [75, 126] are used for this purpose. Recently, several new fragment screening technologies including electrospray ionization and native mass spectroscopy [82, 83], weak affinity chromatography [84], ultrafiltration [87] etc. have been developed to improve the efficiency and throughput of fragment identification and optimization. The overview of some commonly used experimental fragment screening technologies is presented in (Table 2). As can be seen from (Table 2), each one of the experimental fragment screening methods has advantages and disadvantages. For example, protein-detected NMR method [47, 48] is a highly sensitive method for identifying very weakly binding fragments and it also provides 3D structural information of fragment binding to the target protein. However, protein-detected NMR requires high quantities of isotope labeled protein that raises the cost of experiments by many folds. Ligand-detected NMR methods such as STD-NMR [49, 50], WaterLOGSY [51, 52], FAXS [53] and TINS [54, 55] were developed to overcome challenges associated with protein-detected NMR. Ligand-detected NMR methods have their own problems such as high false positive rate and their inability to detect tight binders [47]. X-ray crystallography, a commonly used primary or secondary fragment screening technique provides detailed 3D structural information but requires high quality of protein and well diffracting crystals [127]. Also, crystal soaking with fragments requires high concentration of fragments which is detrimental to crystal. An important disadvantage with protein-detected NMR and X-ray crystallography is that they consume large amount of target protein and fragments and provide no information on binding affinity. SPR based


Fig. (2). Growth evolution of Vemurafenib.

High concentration screening of scaffold molecules against a kinase revealed a series of hit molecules. These hit molecules were co-crystallized with protein kinase domains (represented in white cartoon). One of the first hit fragments, a 7-azaindole scaffold, was able to bind to the active site of the kinase domain (frame 1). This scaffold molecule could be modified (frame 2) and gained in potency. In several rounds of optimization the scaffold was grown into a more potent molecule (frame 3). In the final round several molecules with high affinity were identified. Frame 4 depicts Vemurafenib (PLX4032) which is approved for clinical usage. PLX4032 was selected for clinical development over a more potent derivative, PLX4720 (frame 5) due to its superior PK properties in animal models. Table 2. Strength and Weaknesses of Some of Commonly Used Experimental Fragment Screening Methods

Screening

Method Throughput

Protein

Requirement Sensitivity Advantages Disadvantages

Ligand detected NMR

1000s Medium-high (μM range)

100 nM to 10 mM Highly sensitive, do not require labeled protein Instrument is expensive, false positive rate is

high, cannot detect tight binders

Protein detected NMR

100s High

(50 to 200 mg) 100 nM to 10 mM Provides 3D structure information

Instrument is expensive, require isotope labeled protein, expert required

X-ray crystallography

100s High

(10 to 50 mg) 100 nM to 10 mM Provides detailed 3D structure information

Instrument is expensive, well diffracting high quality crystal requirement, Crystal have to

survive high concentration of fragment while soaking, expert required

Surface Plasmon

Resonance 1000s

Low (about 5 μg)

1nM to 100mM Provides kinetic data like association rate,

dissociation rate alongwith binding affinity (Kd) Protein immobilization on gold surface

required

Isothermal Titration

calorimetry 10s

Low (50-100 μg)

1nM to 1mM Provides highly quantitative affinity data and mechanistic information about non covalent

forces in binding Requires high sample concentration

Native Mass Spectrometry

1000s Low

(about few μg) 10 nM to 1mM

Label free, no need protein immobilization or assay development

Requires careful choice of buffers, problem small molecule aggregation

High Concentration biochemical

screening

>10000 Low

(< 100μg) Not available Simple and straightforward method

Require knowledge of biochemical function, problem of false positives and negatives

technology was developed to overcome these challenges and recent instrumentations like Biacore™ 4000 (GE Healthcare Life Sciences) consumes less protein and fragment and provides reasonable throughput. SPR is a fast and efficient technology now used for primary fragment screening courtesy of the technical advances. Although SPR is highly efficient and provides high quality kinetic data along with binding affinity, controlling the rate of false positives and false negatives is quite challenging [75, 126]. Additionally, SPR requires immobilized target protein and therefore depending upon the immobilization methodology, target could be adversely affected. As seen from (Table 2) experimental methods of

fragment screening have several advantages but despite their immense utility, most of the experimental screening approach can test only up to several hundreds or thousands compounds. The commercially available fragments is much higher (575236 fragments in ZINC database [128]) than what can be tested by any experimental fragment screening approach. Experimental methods of fragment screening also involve huge investment in equipments such as an X-ray machine or beam line, high frequency NMR spectroscopes or surface plasmon resonance equipment. Apart from instrument cost, there is the material cost as sample preparation is expensive for example isotope labeled protein for protein-detected


NMR, protein immobilization for SPR. These methods require experts for maintenance and interpretation of specialized data generated. The limitations associated with experimental biophysical screening and broad application of fragment based drug design present the need for fast and efficient computational methodologies that can complement experimental fragment screening.

6. COMPUTATIONAL FRAGMENT BASED SCREENING

Computational screening also plays an important role in FBDD and a number of computational tools have been developed or adapted to be applied during different phases of FBDD. These computational methods are most useful when they are used in combination with experimental approaches. In silico screening methods are fast, cost efficient and can be applied to a wide variety of biological targets, where high-quality three-dimensional structures are available or can be provided using homology modeling approaches. Similar to the conventional in vitro FBDD, there are three basic steps during in silico fragment based screening. Initially, a fragment library is designed which is then screened by a secondary virtual approach, generally molecular docking. In the final step prioritized fragments are optimized using computational methods for growing, linking or both. Despite the many advantages in computational fragment based screening, there are some drawbacks as well. One of the most serious is the relatively low accuracy of predictions and another is the rapid accumulation of errors. Generally speaking, computational FBDD methods have lower accuracy than experimental FBDD approaches. This could be due to the imperfections in the energy functions used to evaluate protein-ligand interactions. Another reason could be difficulties in achieving complete sampling of the conformational space. Methods that are fast tend to make approximations in conformational searching and the energetic terms used during simulations, and therefore may be less accurate. Whereas more accurate methods that take more exhaustive conformational search and also include protein conformational flexibility tend to be much slower. A strategy to overcome this dilemma is to adopt a layered approach with fast method used first to screen a large collection of compounds and the enriched pool of compounds are then subjected to slower methods for increased accuracy. Although computational method such as docking or molecular dynamics simulation can be performed based on computationally created protein structures using homology modeling, the errors from the homology model coupled with errors in these computational screening methods will significantly lower the reliability of the virtual screening hits. Therefore, the most effective use of computational tools is to combine them with experimental methods and always base computational studies on experimental results and confirm virtual screening hits by experimental assays before the next iteration in computational FBDD. Despite those drawbacks in computational methods, their judicious uses have led to many successful FBDD cases, which will be discussed in the following sections. Integrating computational tools with experimental methods has become indispensible to and a prerequisite for any successful drug discovery effort including FBDD.

6.1. Fragment Library Design

One important point to consider in any fragment screening is the quality of the fragment library, as it has direct effect on the quality of the outcome. One of the well-known laws of computing ‘garbage in, garbage out’ applies well in FBDD and it is very difficult to obtain highly potent compounds as end product without starting the screening with a high quality fragment library. A number of approaches were developed to design or compile fragment libraries with computational tools. The simplest and most commonly used approach to design a fragment library is to filter from commercially available chemicals. Different computational tools such as the Chemistry Development Kit (CDK)[129, 130], the

descriptor calculator plugins from Chemaxon [131], the filter program from OpenEye [132], the sdfilter utility from MOE [133], or QikProp from Schrödinger [134] etc. can be used to filter a library of drug sized or lead-like compounds based on molecular properties such as molecular weight, logP, hydrogen bond acceptor, hydrogen bond donor, rotatable bonds, total polar surface area etc. Other filters include molecular diversity, removal of chemically reactive scaffolds and incorporation of common scaffolds present in drugs. There is no fixed rules for filtering small molecule libraries to get fragment like compounds. Although the “Rule of three” is most commonly used to prepare a fragment library, but as with the in vitro method, exceptions also exist and different groups used different property filters. Several groups developed different methods of computationally generating fragment libraries to be used in computational fragment based screening. These include the pioneering work by Lewell et al. [135] who developed RECAP (Retrosynthetic Combinatorial Analysis Procedure) that computationally produces fragment molecules based on chemical knowledge and retrosynthetic chemistry cleavage rules. Lewell et al. [135] applied RECAP to fragment 35000 compounds from World Drug Index (WDI) and biologically recognized elements and privileged motifs were identified. Using a similar approach, Kolb and Caflisch [136] developed a computer program DAIM (decomposition and identification of molecules) to carry out automatic deconstruction of small molecular libraries to generate fragments. DAIM performs fragmentation of small molecules following the rules similar to RECAP. Degen et al. [137] reported an improved approach for automatic decomposition of molecules into fragments. The approach is known as BRICS (breaking of retrosynthetically interesting chemical substructures) and based on new and more elaborate set of rules for fragmentation. This approach also considers promising chemical motifs and preserves them in fragmentation. Realizing the importance of fragmentation in generating fragment libraries many commercial programs capable of performing in silico fragmentation of small molecule libraries were also developed. These include CoLibri from BiosolveIT [138], Chomp from OpenEye [132], the sdfrag utility in MOE [133], and rule based molecular fragmenting utility from Schrödinger [134].

6.2. Screening of Fragment Libraries by Computational

Methods

The computational methods for screening a fragment library are fast and cost effective as compared to the experimental counterparts. Computational methods can be applied to a wide variety of biological targets if high quality structure is available. Even in the absence of experimental structures, 3D models can be used for virtual screening a fragment library. There are number of computational alternatives to fragment screening and the most common one is molecular docking. Molecular docking is a computational method that predicts the preferred orientation of a small molecule binding to a protein. The virtual screening of drug-like libraries using molecular docking approach is a proven technology in drug discovery. Molecular docking integrated with experimental validation has already provided potential hit compounds for large variety of biological targets [139]. Molecular docking in combination with conventional fragment screening has also been used in FBDD for variety of targets. However, the general belief is that results from docking fragment libraries are not very reliable with present methods and protocols [140]. The reason may be promiscuous binding modes and the inability of scoring function to discriminate near native from irrelevant binding poses [141]. Most of these docking methods and scoring functions were developed for drug-like ligands with molecular weight and other chemical properties significantly different from those of fragments. This may be one of the reasons for the poor performance of the docking algorithms and scoring functions in predicting the binding


mode and affinity of small molecule fragments. To explore the performance of docking methods and scoring functions for docking fragments and drug-like compounds, Verdonck et al. from Astex therapeutics [142] carried out computational analysis examining 206 in-house X-ray structures (106 structures bound with fragments and 100 structures with larger drug-like ligands) from 11 targets. The researchers evaluated the performance of Gold docking program [143, 144] for the docking of fragments and drug-like ligands. Their study revealed no significant difference in docking performance between fragments and drug-like compounds. Their study also revealed that high LE compounds fared better than low LE compounds. This study pointed out the need for normalization of docking scores by dividing by number of heavy atoms to improve the fragment docking capabilities. Other groups also explored the utility of various programs in docking fragment like compounds. This includes a study by Sandor et al. [145] in which they evaluated the docking accuracy of Glide [134] on 190 protein fragment complexes from 78 targets. They used 16 different docking protocols and comparison of results revealed the efficacy of Glide sampling and scoring is sufficient to dock fragment like compounds. Kawatkar et al. [146] also evaluated Glide with various scoring schemes for fragment docking capabilities. They docked fragment inhibitors of prostaglandin D2 synthase and DNA ligase to their respective target proteins along with experimentally determined inactive fragments. The enrichment of actives over the inactives was used as performance measure. The results of their study indicate that fragment docking using Glide can yield enrichments significantly better than random. Our group has also evaluated a virtual fragment screening protocol including the flexible docking program RosettaLigand [147-149] as a core component. Our study found that results from RosettaLigand [147-149] display a similar performance for fragment like ligands as for drug-like ligands. The chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges [150]. There are number of studies reported in literature that identified potent fragment molecules utilizing structure based docking followed by experimental validation [140, 151-153]. Shoichet and coworkers [140, 153] used docking based fragment screening on two protein targets: CTX-M

-lactamase and AmpC -lactamase. In the first study, Chen and Shoichet [140] used DOCK 3.5.54 [154, 155] to dock 67,489 commercially available fragment like compounds from ZINC database against CTX-M -lactamase, a bacterial enzyme responsible for antibiotic resistance. They tested 69 top scoring fragments experimentally, and ten fragments inhibited the enzyme in mM range. Crystal structures were obtained for five of these fragments and corresponded closely to their docked poses. Further search for derivatives of these fragment led to the identification of the first μM range inhibitor for CTX-M -lactamase. A second study by Teotico et al. [153] describes the identification of fragment inhibitors for AmpC -lactamase, another bacterial protein responsible for antibiotic resistance. They followed the same strategy to dock 137,639 fragments against AmpC beta-lactamase. The docking screen resulted in the selection of 48 fragments for biological evaluation, out of which 23 had Ki values ranging from 0.7 to 9.2 mM. Ruda et al. [156] carried out another successful virtual fragment screening to identify inhibitors of 6-phosphogluconate dehydrogenase, a potential drug target against human African trypanosomiasis. They first filtered ACD–SC database for compounds that were negatively charged and an MW less than 320 Da. The filtered compound library was subsequently docked to 6-phosphogluconate dehydrogenase using DOCK 3.5.54. Seventy one high scoring compounds were selected after clustering and visual inspection, and were acquired and subjected to evaluation using a biological assay. The experimental testing resulted in three compounds that inhibited protein significantly with

IC50 in low μM range. Commercially available derivatives of these active hits yielded more active compounds. In another study, Mpamhanga et al. [157] used DOCK 3.5.54 to dock a library of 26084 fragment molecules into Trypanosoma brucei Pteridine reductase 1 inhibitor binding site and 10 active fragments were found after biological validation. The most active fragment showed a potency of 10.6 μM. This fragment was characterized by X-ray crystallography and was grown into a 7nM potency compound. Apart from fragment screening, molecular docking has been used to predict the binding mode of fragment hits where the experimental fragment screening technique does not provide structural information of fragment binding. Protein ligand structural information is crucial to optimize an initial fragment with weak affinity to a highly potent lead compound. There are some recent examples in the literature where molecular docking provided reasonable SAR to guide further fragment optimization [158-161]. One example is the study by Barelier et al. [159] where they screened a library of 200 fragments against human peroxiredoxin 5 protein using STD NMR and WaterLOGSY experiments. NMR based screening resulted in the identification of 6 fragments that bind to peroxiredoxin 5 protein. The binding mode of these fragments was characterized by molecular docking and NMR data, and was confirmed using derivatives of these fragments.

Several other molecular docking based methods were also developed by different groups to enhance efficiency of FBDD. Caflisch and coworkers [162-169] developed a fragment based screening method where a library of small molecules, such as ZINC, is decomposed into fragments which are then docked into the target protein. In the next step, a library of small molecules is flexibly docked into the target protein using the position of their corresponding fragments as anchors which are ranked by binding free energy. This approach used three different programs DAIM, SEED and FFLD to perform each step. Although their approach gives a complete molecule as the final output but, it can be fully customized to get fragment binding poses to carry out fragment linking studies. They have applied this fragment screening method to identify inhibitors of six different proteins including -secretase [162, 163], plasmepsin [169], NS3 protease [167, 168], Cathepsin B [166], cyclin-dependent kinase 2 (CDK2)[164] and erythropoietin producing human hepatocellular carcinoma receptor tyrosine kinase B4 (EphB4)[165] to identify single digit μM inhibitors. In another study, Fukunishi et al. [170] developed a Fragment Screening by Replica Generation (FSRG) method which is the integration of fragment identification and fragment evolution steps of FBDD. In their approach, a fragment library is first constructed from which replica molecules are generated by adding several substituents. These replica molecules are then docked to the target protein and evaluated by scoring based on surface complementarity between protein and small molecule. They have tested their approach on six targets and it showed promising results. Brenke et al. [171] developed FTMap algorithm to primarily identify hotspot regions for small molecule binding in proteins. In their algorithm, sixteen fragment probes with varying hydrophobicity and hydrogen bonding capabilities are used to map protein surface for identifying energetically favorable binding regions. A fast Fourier transform approach is used to dock fragments onto the protein surface and their interaction energy is calculated using an energy function which is dominated by the terms for attractive and repulsive van der Waals, electrostatic term obtained after solving the Poisson-Boltzmann equation, a nonpolar term and a term representing the structure based pairwise interaction potential. FTMap can be used to dock selected fragments and these docked fragments can serve as starting point for fragment based drug design. Also, the identified hotspot regions could guide fragment optimization studies. Imai et al. [172] used the 3D reference interaction site model (3D-RISM) theory to develop a novel computational method for mapping fragments on the surface of proteins. They demonstrated the application of their method on thermolysin and showed that their


method can accurately reproduce binding modes found in X-ray crystallographic studies. Pharmacophore modelling based virtual screening approaches are established method for screening libraries of lead-like compounds [173]. The pharmacophore based methods have also been applied in the field of FBDD for the identification of potent fragments for further optimization. In one such study, Fritzson et al. [174] used structure based focusing followed by experimental assay to identify inhibitors of human dihydro-orotate dehydrogenase (DHODH), a mitochondrial enzyme involved in biosynthesis of pyrimidines. Receptor based pharmacophores were generated using the LUDI interaction map of the binding site in the absence of inhibitor. These pharmacophore queries were then used to screen Accelrys’s NCI and Maybridge databases and the hits were subjected to molecular docking using LigandFit. Biological evaluation of 265 fragments selected from virtual screening hits resulted in the identification of 4-hydroxycoumarins, fenamic acids, and N-(alkylcarbonyl)anthranilic acids as novel inhibitor classes against human DHODH. Further expansion of these chemical classes resulted in the identification of one compound with potency in nM range. Ji et al. [175] also used a pharmacophore driven virtual screening strategy to identify nM potent and 1000 fold selective inhibitor of neuronal nitric oxide synthase (nNOS), an enzyme implicated in stroke and neurodegenerative diseases. They identified the minimum pharmacophore features required for nNOS activity and selectivity using a combination of different pharmacophore identification methods. This pharmacophore query was searched against a fragment library to find all of the possible fragments that are able to match the requirements of the minimal pharmacophore features. Further docking and in silico linking of fragments using LUDI and molecular docking of linked molecules using Autodock generated the candidates for chemical synthesis. The biological evaluation of synthesized candidate molecules resulted in many active hits with one molecule displayed Ki value of 388 nM against nNOS and 1000 fold selectivity over eNOS and iNOS.

6.3. Computational Methods for Optimization of Fragment Hits

The identification of an initial fragment hit is the first step in FBDD and after this, the next goal is to optimize initial hit into a lead compound with high potency against target proteins. Several computational methodologies were developed that can be used in different stages of fragment optimization. The most commonly used approach to optimize hit fragments into high affinity compounds is to look for similar compounds in commercially or publically available small molecule databases. Typically, substructure and/or similarity searches are used for this purpose. This strategy of fragment optimization using computational tools has been quite successful and many potent compounds were identified [88, 176]. Other approaches include the usage of de novo design programs that are capable of automatically building new ligands either by growing from initial fragment or by linking two or more non overlapping fragments. The de novo design approach has been successfully used earlier for the discovery of drug-like molecules [177] and in principle, can be applied for fragment based drug design as well. There are several commonly used de novo design programs including LUDI [178], GROWMOL [179], PRO_LIGAND [180], LigBuilder [181, 182], SMoG [183], Allegrow [184], GANDI [185], CONFIRM [186], Autogrow [187], FOG [188] etc. that can be used to optimize fragment hits into lead compounds.

GroupBuild was one of first fragment based de novo design program developed by Rotstein and Murcko [189]. It starts from a user defined seed atom or pre-docked inhibitor core to grow fragments. GroupBuild uses a library of organic molecules to generate candidates molecules which are ranked based on a standard molecular mechanics potential function. Dey and Caflisch [185] developed GANDI, which is a genetic algorithm based de

novo design program that searches for linker molecules to join fragments pre-docked in the target binding site. GANDI uses an approach where it simultaneously minimizes the force field energy and the 2D/3D similarity to known inhibitors. Another program, CONFIRM [186] screens a prepared ‘bridge library’ to find out linkers between two fragments. Bridges are linked to fragments using combinatorial approach and linked molecules are computationally docked to receptor binding site. Top linked molecules are selected on the basis of root mean squared deviation from the initial positions of the fragments. Pierce et al. [190] from Vertex Pharmaceuticals developed the BREED algorithm which is an automated computational method to join different fragments from known ligands to create new inhibitors. This BREED algorithm has been successfully used by Vertex Pharmaceuticals to generate novel inhibitors of CDK2, p38 and HIV protease. The BREED program is now licensed to Chemical Computing Group and is available as a MOE implementation. Other commercially available fragment linking alternatives include LeadIT [138], Brood [132], or MED-Hybridise [191]. These programs search a database to obtain compounds in which two initial fragments are linked with a suitable linker. The hit compounds are ranked by different scoring approaches, such as docking based scoring, synthetic feasibility score, RMSD displacement of the original fragments, strain energy, electrostatic similarity etc. De novo design programs can be also used to grow from initial fragments to generate compounds with better potency. Durrant et al. [187] developed a fragment growing algorithm, Autogrow that starts growing by adding fragments randomly to initial core fragment which are dynamically docked into the protein. Autogrow is based on a genetic algorithm, thus the best bound molecules, according to the docking score, become the parent scaffold for subsequent generations. The Autogrow algorithm was validated by recreating a few substrate and inhibitor molecules from an initial core scaffold. Kutchukian et al. [188] developed an algorithm, Fragment Optimized Growth (FOG), which uses a Markov chain approach to add fragments to the starting molecule in a biased manner. The statistical bias was generated based on the frequency of certain features that appear with high occurrence in the training database. Their algorithm also takes care of the synthetic feasibility, the shape and the energetic complementarity of the growing fragments.

Lai and coworkers [181, 182] developed the Ligbuilder program that has both fragment linking and fragment growing functionalities. Ligbuilder uses a genetic algorithm to construct ligands iteratively using a library of organic fragments. Ligbuilder also analyzes the synthetic feasibility of designed compounds by using an embedded chemical reaction database and a retro-synthesis analyzer. The de novo design efficiency of Ligbuilder was demonstrated by designing and optimizing Cyclophilin A inhibitors [192]. In this study, the researchers used Ligbuilder to grow a full inhibitor from an acylurea seed fragment based on shape and energetic complementarity to the Cyclophilin A binding site. Among many hits, one hit was selected for synthesis and evaluated for its ability to inhibit Cyclophilin A that eventually lead to the identification of a highly potent lead compound displaying IC50 of 31.6 ± 2.0 nM against Cyclophilin A. This compound was subsequently optimized by chemical modifications which led to the discovery of two most potent Cyclophilin A inhibitors with IC50 value of 2.59 and 1.52 nM.

Li et al. [193] presents an interesting case of computational fragment based drug design and a novel method of drug discovery where they simultaneously docked privileged drug scaffolds into hotspot regions of STAT3, a cancer target. MLSD program [194] was used to dock these scaffolds and combined predicted binding energy was used to rank the top fragments. The two fragment combinations were selected for in silico linking using different chemical tethers such as amide, amine, ether, and olefin and 15 virtual compounds were generated. A similarity search of these 15


virtual compounds against Drugbank [195] identified celecoxib as a novel inhibitor of STAT3. One of the in silico linked hits and one celecoxib derivative were synthesized that inhibited STAT3 with an IC50 in the low μM range. Knehans et al. [196] used an implicit fragment linking approach to optimize high scoring fragments from virtual screening of fragment library against dengue protease. In their approach, selected fragments were assembled by implicit fragment linking cascade which included substructure search and structural filters focusing on critical interactions. Neighbor searching was also used to improve the coverage of chemical space. The experimental testing of 23 compounds resulted in two inhibitors of dengue protease with IC50 value of 7.7 and 37.9 μM. Computational tools for combinatorial chemistry have also been used to optimize initial fragments. Card et al. [27] from Plexxikon, used another approach for chemical optimization of validated scaffolds identified using high concentration screening and X-ray crystallography against PDE4. They generated virtual libraries around a validated scaffold using specific chemical reagents and reaction schemes. The virtual libraries were ranked by molecular docking and molecular dynamics based MM-PBSA calculations. Ten high ranking compounds were synthesized and tested for PDE4 inhibition. The biological evaluation resulted in the identification of 4 compounds in nM range which were confirmed by X-ray crystallography. Using this combinatorial approach, the IC50 of initial fragment was improved from 82μM to 21nM for the optimized compound.

7. RECENT SUCCESS IN COMPUTATIONAL FRAGMENT

SCREENING

In recent years, several FBDD studies have been reported in literature that used computational fragment screening integrated with experimental approaches for the identification and optimization of fragments. An overview of some of the recent successes is presented in (Table 3) that includes work by Taylor et al. [104] who carried out virtual screening of an in-house library to identify fragment hits that can bind S1' pocket of MMP-13, a member of MMPs with apparent role in rheumatoid arthritis. The high scoring hits from virtual screening were enriched with some diverse scaffolds to prepare a library of approximately 1000 fragments that was further characterized using biochemical screening, STD NMR, size exclusion chromatography and mass spectrometry. One best hit that came from virtual screening displayed Kd value of 39 μM and LE of 0.35 and was selected for optimization. The crystal structure of this initial hit also revealed that it binds in the S1' pocket of MMP-13. Fragment growing was selected as the method for optimization and this initial hit was grown into 1 nM potency compound with 1000-fold selectivity against 9 other MMPs. Tanaka et al. [197] docked an internal fragment library to anti-inflammatory target epoxide hydrolase using the Glide program. Their virtual screening led to the selection of 735 compounds, which were tested experimentally. The biological evaluation resulted in 68 compounds displaying IC50 values less than 1μM. Fragment hits with very good LE were chosen for further optimization by X-ray crystallography and synthesis of analogs that resulted in the identification of much potent compound with good ADME properties. Virtual screening other than molecular docking has also been used to complement fragment screening by experimental methods. Hughes and coworkers [88] from Pfizer used computational substructure screening, along with high concentration biochemical screening, in their FBDD efforts to identify PI3K inhibitors. They first used high concentration biochemical screening (at 0.5-1.5 mM) to screen 5960 fragments from the Pfizer library to identify hits inhibiting PI3K . Selected hits were confirmed by an isothermal denaturation assay followed by X-ray crystallography, and resulted in the identification of a hit with IC50 value of 915μM and LE of 0.35. This initial hit, with weak binding affinity towards PI3K , was

subjected to virtual screening against the Pfizer solid compound collection using substructure and nearest neighbor approach, which resulted in compounds more potent and with high LE than the initial hit. Furthermore, the compounds identified from virtual screening guided fragment growing and merging which resulted into a compound with IC50 value of 34nM against PI3K with fairly good selectivity profile when tested against 43 kinases.

G protein coupled receptors (GPCRs) are a very important family of drug targets but until now [46, 77] FBDD was not very successful except a few studies [122, 123]. This may be because of their conformational flexibility and instability outside the cell membrane. New technologies like “STAR technology” [198-201], SPR and TINS have shown encouraging results in FBDD against GPCR targets. Computational tools of fragment screening have a great advantage of not being affected by the problems as faced by experimental screening. There are some studies reported in literature that have used computational tools for GPCR’s FBDD. In one such study, de Graaf et al. [202] developed and validated a virtual screening protocol that combines molecular docking with protein ligand interaction fingerprints. This validated protocol was then used to identify fragment like histamine H1 receptor ligands. They carried out molecular docking of 108790 compounds extracted from ZINC database [128] based on “Rule of three” and containing a basic moiety to facilitate ionic interaction with critical Asp107 of histamine H1 receptor. The hits from molecular docking using PLANTS [203] were further prioritized using protein ligand interaction fingerprints taking 9 reference compounds with known activity on the H1 receptor. A set of 26 fragment-like compounds was experimentally tested, and 19 compounds displayed affinities ranging from 10 μM to 6nM for histamine H1 receptor. Similar to targeting GPCRs, in silico fragment based screening can be very effective in the identification of small molecule protein-protein interaction inhibitors, as indicated in the subsequent section.

8. FRAGMENT BASED DRUG DESIGN AS A PREFERRED METHOD FOR THE DISCOVERY OF SMALL MOLECULE

PROTEIN-PROTEIN INTERACTION INHIBITORS

Due to the advances in proteomics in recent years, we have learned that most proteins do not act in solitude but interact with other proteins. These protein-protein interactions (PPIs) are crucial elements of biological processes. Since many disease-related pathways are influenced by PPIs, targeting PPI has become a new trend in pharmaceutical research. While the pharmaceutical industry has been successful in designing drug-like molecules to target enzymatic active sites, influence ion channels and transporters, or modulate receptor activities, there is not a single Small Molecule Protein-Protein Interaction Inhibitor (SMPPII) approved for clinical usage. As a matter of fact, PPIs were once thought to be undruggable by the pharmaceutical research community [204, 205]. Recently however, a considerable progress has been made in the design of SMPPIIs [206].

The PPIs represent a more complex system for the design of small molecule inhibitors than enzymes. For example, enzyme inhibitors commonly bind in clear cavities/clefts and bind with contacts similar to those present in the substrates. Therefore, the enzyme substrates can serve as templates for the design of inhibitors. This is however not the case when targeting PPIs; there are no substrates that can serve as templates. Furthermore PPIs have large surface areas, which is typically 1500-3000 Å2 compared to the 300-1000 Å2 of interaction interface between small molecules and proteins. Also, PPIs are often composed of non-contiguous residue stretches and thus cannot be mimicked by simple synthetic peptide [207]. While the number of SMPPIIs is still limited, the majority of these hit molecules originate from exhaustive screening strategies rather than rational design [208]. In fact, examining the chemical space occupied by SMPPIIs revealed a discrepancy with


the chemical space of drug-like and hence also commercially available molecules [209, 210]. FBDD however does not suffer from such limitations since it’s easier to cover the chemical space within the molecular weights range and this could be a great screening tool for the discovery of SMPPIIs. In fact it should be noted that FBDD was the method of choice used to create some of the most well-known SMPPIIs.

Currently, the SMPPII showcase of choice is the inhibitors of the Bcl2 family of proteins. These anti-apoptotic proteins bind to their pro-apoptotic protein partners through the interaction of alpha helices, thereby preventing the induction of apoptosis. Currently a SMPPII compound (ABT-263) targeting this PPI is in phase 3 clinical trials for cancer. The origins of this compound can be traced back to the previously mentioned (see section 3.1.1 and 3.2.2) “SAR by NMR” approach as reported by Petros et al. [211]. As mentioned before, Hu et al. [116] also identified a SMPPII targeting a similar interaction, by using the Bcl-XL protein as a template for in situ click chemistry, another FBDD strategy. For the optimization of a SMPPII targeting the interleukin2 (IL2) IL2-receptor interaction, Braisted et al. optimized an inhibitor using a fragment based tethering method [92, 94]. An initial low molecular weight hit molecule (Ro26-4550) with an activity of 3 M was used as the starting point. The crystallographic complex revealed that this molecule binds to IL2 by inducing a cleft on the surface. Based on this crystal structure cysteine mutants were designed aligning the hotspot binding residues. These mutants were screened against a library of 7000 disulfide containing fragments. Screening the mutant proteins with disulfide containing ligands will automatically covalently link with an adjacent sulfide. Analysis indicated that two cysteine mutants selected structurally related fragments, while the other cysteine mutants were unable to select any relevant fragments at all. Further structural analysis of the fragment binding to the receptor protein and overlay with the first hit molecule led to the

design of improved inhibitors with 60nM affinity, a fifty fold improvement compared to the first hit molecule.

Abdel-Rahman et al. recently reported the usage of crystallographical screening as a method to probe druggable PPIs [212]. Their target was the Notch transcription complex, where the ternary complex is composed of an extensive network of protein-protein interactions. The complex consists of CSL (CBF1/suppressor of Hairless/Lag-1) and MAML (Mastermindlike) bound to the Notch ankyrin domain. Through the crystallographical fragment based screening technique they were able to identify a series of compounds that bind to the Notch ankyrin domain by exploiting similar interactions present in the Notch complex. These fragments can serve as starting points for the development of novel inhibitors of the Notch complex which is implicated in the development of T-cell acute lymphoblastic leukemia (T-ALL).

While previous examples are based on in vitro fragment identification, the computational approach has also been successful for the discovery of SMPPIIs. For example, the inhibitors of the XIAP caspase interaction originated from traditional peptidomimetics design. The first reported SMPPII targeting this family of PPI evolved from the peptide fragment that binds to the XIAP by forming an antiparallel beta-plate. The design of highly potent inhibitors involved many synthetic intermediates and redesign efforts before the first compounds with a promising PK were reported [213]. On the other hand, Huang et al. reported the discovery of more drug-like XIAP inhibitors after a short and successful FBDD setup [152]. A virtual library of 1383 drug-like fragments was created by linking the crucial L alanine to 1383 drug-like amino-decorated scaffolds. These compounds were virtually screened against the target by docking. The promising compounds were synthesized and screened using NMR. A weakly interacting (200 M) inhibitor was identified which was used for a second round of inhibitor design. After structural analysis of the

Table 3. An Overview of Recent Success with Computational Fragment Based Screening

Target Initial Fragment Computational

Method

Experimental

Method Optimized Fragment Reference

MMP-13 NH

H2N

OO

O

Kd = 39 μM, LE = 0.35

Molecular Docking

Biochemical Screening, STD NMR and Size

Exclusion Chromatography

Mass-Spectrometry

NH

O

O

NN

HN

O

O

OH

Kd = 0.001 μM, LE = 0.37

Taylor et al.,

(2011)

PI3K N

N

N

H2N

IC50 = 915 μM, LE = 0.35

Substructure and Nearest Neighbor

Search

High Concentration Biochemical Screening, X-

ray Crystallography

N

N

HN

O

S

NH

O

O IC50 = 0.034 μM, LE = 0.48

Hughes et al.,

(2011)

Histamine H1

Receptor

O

HN

Ki = 6nM

Molecular Docking.

Protein ligand interaction fingerprint

Biochemical Assay, X-ray

Crystallography Yet to be Optimized

de Graaf et al.,

(2011)

Epoxide Hydrolase

O

NH

CN IC50 = 0.565 μM, LE = 0.43

Molecular Docking

Biochemical Screening

N

O

NH

N

N O IC50 = 0.0085 μM, LE = 0.42

Tanaka et al.,

(2011)


compound using molecular docking and overlay with the peptide ligand, a new virtual library of 900 derivatives was designed. Again the promising ligands were selected after docking and synthesized, and evaluated by NMR spectroscopy. The active molecule was further optimized resulting in a compound with 1.2 M affinity. Since this compound is more drug-like than its peptidomimetics counterparts it is an interesting starting point for further medicinal chemistry optimization.

Agamennone et al. [214] used a combination of several computational approaches to identify suitable scaffolds for the development of inhibitors of S100B-p53 PPI. S100B has an important role in tumor genesis as it binds at the C-terminus of p53 and inhibits its tumor suppressing activity. Agamennone et al. [214] used GRID molecular interaction field [215] based screening, ligand based pharmacophore and GOLD [144] based molecular docking to screen a library of fragment like compounds compiled from 168 different suppliers. A total of 280 high scoring fragments selected from each of the individual computational method were subjected to NMR screenings which confirmed five selective binders with an estimated Kd value in the 0.1 to 1.4mM range and LE in the range of 0.19 to 0.26. The X-ray crystal structure was obtained for one of the binders to guide future fragment optimization.

In another approach Cavalluzzo et al. [216] designed a novel inhibitor for the LEDGF/p75 HIV-1 Integrase interaction. For this de novo design strategy they examined the PPI interface and identified 3 key amino acids on an interaction alpha helix. The two extreme amino acids were considered as fragment, since they form key interactions involving hydrogen bridges and hydrophobic interactions. The middle one, of the 3 key amino acids, only formed hydrophobic interactions. Using this information, molecules were designed by employing the “pharmacophore guided scaffold replacement”, which aimed to link the two amino acid fragments by connecting them with another fragment. All possible options were assessed in silico and the synthetically most feasible as well as most complementary according to docking simulations was created. This de novo design strategy relied on in silico fragment identification and linking. Interestingly this compound targets the LEDGF/p75 protein, while in a previous paper the same research group also reported inhibitors for this interaction, after using a classic non fragment based virtual screening method, targeting the integrase counterpart [217]. This makes the LEDGF/p75 integrase interaction the first case where SMPPIIs were designed for both interaction partners. Of note, Cavalluzzo et al. [216] assign the success to the fragment based approach, which is more suited for challenging targets such as LEDGF/p75, which lacks a clear pocket compared to the integrase target.

As the previous cases indicate, FBDD is a suitable approach for the challenging PPI targets. A variety of methods are available to screen for the first hit molecules while one does not need to worry whether or not the correct chemical space is present in the screening library.

CONCLUSION

Fragment based drug design (FBDD) is a powerful and widely used drug discovery approach. It involves the identification of low molecular weight chemical fragments and their optimization into lead compounds. Over the past two decades, there are numerous examples in the scientific literature demonstrating the utility of fragment-based methods in generating high quality lead compounds for a variety of targets. The success in fragment based drug discovery owes to recent technological advancements in fragment screening technologies. X-ray crystallography and NMR based screening strategies were particularly successful in identifying highly ligand efficient fragment hits that could serve as scaffolds for the generation of potent lead compounds for many targets. Also,

the use of recent technologies like SPR based biosensor assay made possible the high-throughput label-free screening of fragments against membrane proteins such as GPCRs. Despite several advantages of experimental fragment screening methodologies, its applicability is limited because of the cost associated with experiments, high protein and fragment requirement, low throughput nature and limited target applicability. Alternative screening technologies like computational fragment screening can complement experimental fragment screening to combat these challenges. Computational methods have already played important roles both in selecting the initial fragments and in the structure guided optimization of fragments identified by experimental fragment screening methods. Although computational methods are based on approximations and are less accurate then experimental methods but they can produce considerably good results if used in combination with experimental methods. The utility of computational screening and optimization methods in FBDD is exemplified by several studies. These include the successful identification of fragment hits against difficult drug targets such as GPCRs and PPIs. However, the applicability of computational approaches in FBDD is still in early stage and more appropriate fragment screening and optimization methods need to be developed. These include programs for generating target-focused fragment libraries, customized scoring functions for docking, de novo design programs for the generation of synthetically feasible compounds etc. These developments, together with advances in current experimental screening technologies, will extend FBDD to a wider range of biological targets and it is anticipated that FBDD will play a larger role in drug discovery by improving the quality of lead compounds, the efficiency in lead optimization and the productivity in finding better drugs that benefit patients.

CONFLICT OF INTEREST

The authors have declared that no conflict of interests exists.

ACKNOWLEDGEMENTS

We thank members of Zhang Initiative Research Unit for help and discussions. We acknowledge the Initiative Research Unit program from RIKEN, Japan for funding.

ABBREVIATIONS

SBDD = Structure Based Drug Design

FBDD = Fragment Based Drug Design

HTS = High Throughput Screening

LE = Ligand Efficiency

NMR = Nuclear Magnetic Resonance

SPR = Surface Plasmon Resonance

ITC = Isothermal Titration Calorimetry

mM = Millimolar

μM = Micromolar

nM = Nanomolar

IC50 = Half Maximal Inhibitory Concentration

PPI = Protein-Protein Interaction

SMPPII = Small Molecule Protein-Protein Interaction Inhibitor

GPCR = G-Protein Coupled Receptor

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Fragment Based Drug Design: From Experimental to Computational