The Many Roles of Computation in Drug Discovery

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Outline

• Overview of computer aided drug designing.• Clinical and Pre-clinical trials.• Prediction of properties and Drug-likeness.• Advanced treatments of protein-ligand

binding.• Summary

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Computer aided drug designing.

• Drug design with the help of computers may be used at any of the following stages of drug discovery:– hit identification using virtual screening (structure-

or ligand-based design)– hit-to-lead optimization of affinity and selectivity

(structure-based design, QSAR, etc.)– lead optimization optimization of other

pharmaceutical properties while maintaining affinity

3http://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design

Computer aided drug designing.

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• the classical project-collaboration approach between chemistry, biology and drug metabolism (ADME) groups in the 1990s

• a much more automated world at the start of this millennium in which combinatorial chemistry (Combi-Chem), high-throughput screening and ADME studies are linked together in a streamlined fashion.

ADMET in silico modeling towards Prediction Paradise?, Nature

Computer aided drug designing.• In order to overcome the insufficient prediction of binding affinity

calculated by recent scoring functions, the protein-ligand interaction and compound 3D structure information are used to analysis. For structure-based drug design, several post-screening analysis focusing on protein-ligand interaction has been developed for improving enrichment and effectively mining potential candidates:– Consensus scoring

• Selecting candidates by voting of multiple scoring functions• May lose the relationship between protein-ligand structural information and

scoring criterion– Geometric analysis

• Comparing protein-ligand interactions by visually inspecting individual structures

• Becoming intractable when the number of complexes to be analyzed increasing

– Cluster analysis• Represent and cluster candidates according to protein-ligand 3D information• Needs meaningful representation of protein-ligand interactions.

5http://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design

Clinical Trials• Sets of tests in medical research and drug development

that generate safety and efficacy data• Only after satisfactory information has been gathered on

the quality of the nonclinical safety, and health authority/ethics committee approval is granted

• The most commonly performed clinical trials evaluate new drugs, medical devices (like a new catheter), biologics, psychological therapies, or other interventions.

6http://en.wikipedia.org/wiki/Clinical_trial

Clinical Trials• Clinical trial may be designed to do:

– Assess the safety and effectiveness of a new medication or device on a specific kind of patient

– Assess the safety and effectiveness of a different dose of a medication than is commonly used

– Assess the safety and effectiveness of an already marketed medication or device for a new indication

– Assess whether the new medication or device is more effective for the patient's condition than the already used ("the gold standard" or "standard therapy")

– Compare the effectiveness in patients with a specific disease (e.g., device A vs. device B, therapy A vs. therapy B)

http://en.wikipedia.org/wiki/Clinical_trial7

Clinical Trials• 5 different types

– Prevention trials • find better ways to prevent disease in people (medicines, vitamins, vaccines,

minerals, or lifestyle changes)

– Screening trials• test the best way to detect certain diseases or health conditions.

– Diagnostic trials • find better tests or procedures for diagnosing a particular disease or condition.

– Treatment trials• test experimental treatments, new combinations of drugs, or new approaches to

surgery or radiation therapy.

– Quality of life trials • ways to improve comfort and the quality of life for individuals with a chronic

illness.

– Compassionate use trials • provide partially tested, unapproved therapeutics to a small number of patients

who have no other realistic options.


Clinical Trials• Clinical trials involving new drugs are commonly classified into

four phases. – Phase 0: Pharmacodynamics and Pharmacokinetics– Phase 1: Screening for safety– Phase 2: Establishing the testing protocol– Phase 3: Final testing– Phase 4: Post approval studies

• Each phase has a different purpose and helps scientists answer a different question:

• Before pharmaceutical companies start clinical trials on a drug, they conduct extensive preclinical studies.


Preclinical Trials• Before clinical trials, during which important feasibility,

iterative testing and drug safety data is collected• To determine a product's ultimate safety profile

– Pharmacodynamics (what the drug does to the body) (PD)– pharmacokinetics (what the body does to the drug) (PK)– ADME– toxicity testing through animal testing

• Both in vitro and in vivo tests will be performed• Based on pre-clinical trials, No Observable Effect Levels

(NOEL) on drugs are established, which are used to determine initial phase 1 clinical trial dosage levels on a mass API per mass patient basis.

http://en.wikipedia.org/wiki/Pre-clinical_development10

Prediction of Properties and Drug-likeness

• Credits and Thanks for raising the awareness on the properties and structural features– Lipinski, Murcko, co-workers at Pfizer and Vertex

• Main Goal = Apply MADE early in pre clinical development to avoid late stage failures

• Analyses of classes of compounds are informative• Avoidance of the extremes seems to be safe strategy

The Many Roles of Computation in Drug Discovery, Science11


• Why?• Which?• How?• When?• What?– Predict– Tools

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Why computational ADME required?• Traditional drug designing is a Multi step Time consuming

process.

• Adverse pharmacokinetic properties were investigated in development stage.

ADMET in silico modeling towards Prediction Paradise?, Nature13

• The rate at which biological screening data are obtained has dramatically increased

• Combinatorial chemistry feeds these hit-finding machines

• Increased the demands for absorption, distribution, metabolism, excretion and toxicity data early

Why computational ADME required?• Attrition in the drug development

• Early decision


Why computational ADME required?

• The promising compound went over the line and was abandoned in the later stage due to its oral bioavailability of only 1%.

Peptide like thrombin inhibitor


Which properties make drugs differentfrom other chemicals?

• Numerous studies• Influential one = LIPINSKI’s “Rule of Five”

• Mol mass < 500 da• Calc octanol/water

partition coefficient < 5• H-bond donors <5• H-bond acceptors <10

• Physicochemical and Structural Properties characteristic of a good drug


Which properties make drugs differentfrom other chemicals?

• These properties = Build ADME models == Property based design

• Similar molecules =~= Similar ADME properties• Predict properties like

– Lipophilicity– Solubility– Amount absorbed


How are ADMET data obtained?• Three ways

– Automated in vitro assays– In silico selection of both the relevant assays and the compounds that go

through them– Predictive models that can possibly replace in vitro or in vivo experiments

• The predictions come from regression equations or neural networks

• QikProp – Fast and executed for large libraries– Input is 3D structure– Output is profile of

• Structural features (Surf Area and H bonding potentials)• ADME properties• Undesirable functionality• Primary metabolites• Comparison with other drugs


How are ADMET data obtained?

• The two drugs approach hydrophobic and Hydrophilic extremes• Hydrophobic

– Poor solubility– High serum protein binding– Good Cell permeability

• The Opposite is true in case of hydrophilic compounds• This is responsible for the solubility vs. permeability struggle


When is ADMET data needed?

Design of New compounds

Need of properties

Traditional or Combo Chemistry

• Predictions are not perfect at this point.• series of molecules is focused around a lead and is further

optimized towards a clinical candidate, more robust mechanistic models will be required.


What ADME properties do we want to predict?

DOSAGE Amount DOSAGE Frequency

Volume of DistributionVolume of

Distribution ClearanceClearance AbsorptionAbsorption

Oral bioavailability

Oral bioavailabilityHalf LifeHalf Life


What computational tools are used?Molecular Modeling

• Protein Modeling that uses Quantum mechanical methods for interaction study

• 3D structural info needed• No structure available

– Homology modeling (related structures)

– Pharmacophore modeling (superposition of known substrates)

Data Modeling• QSAR and QSPR with

biological and physicochemical data– search for correlations

between a given property and a set of molecular and structural descriptors of the molecules in question

• QSAR– Mol size– H bonding– simple multiple linear regression

to modern MULTIVARIATE ANALYSIS techniques


What computational tools are used?


What computational tools are used?

• UC – 781 is most hydrophobic. It is potent in vitro. It is seen as microbicide than oral drug.– Poor solubility and high serum binding

• Long BB predictions are also interesting from standpoint of potential CNS penetration.– Beneficial for attack on HIV reservoirs– Concern CNS side effects



• Good predictive models for ADMET parameters depend crucially on– selecting the right mathematical approach– the right molecular descriptors for the particular

ADMET endpoint– sufficiently large set of experimental data for the

validation of the model• Several published ADME data sets are available for data

modeling, but the quality of the data and the number of available training examples remain important issues.


Advanced treatments of protein-ligand binding.

• For obtaining better accuracy and to match computing demands

• Monte Carlo statistical mechanics or Molecular Dynamics simulations are applied– Classical force field is used– Sampling all degrees of freedom of complexes– Representation of water molecule in aqueous

surroundings



• Free Energy Perturbation and Thermodynamic Integration compute free energy changes.

• Perturbations are made to convert one ligand to another using Thermodynamic cycles

• Δ Δ Gb = Δ GX – ΔGY = Δ GF – ΔGC gives the difference in free energies of ligands binding X and Y

• Two series of mutations are performed to convert X to Y unbound in water and complexed with biomolecule which yield Δ GF and ΔGC.

• Same cycle can be performed with One inhibitor and 2 proteins



• Inclusion of water makes the representation more realistic• water molecules that form hydrogen-bonded bridges

between inhibitors and protein hosts are well illustrated• Water molecules form specific clathrate-like networks

around nonpolar groups• Optimization of the water structure is becoming a more

common part of inhibitor design• Other Strengths

– rigor– extensive sampling– binding affinities.

• remains difficult to handle large structural differences between ligands as in changing the core structure



• Hybrid Methods– Linear response theory– free energy of interaction of a solute with its environment is given by one-half

the electrostatic energy plus the van der Waals energy scaled by an empirical parameter

– For binding a ligand to a protein, the differences in the interactions between the ligand in the unbound state and bound in the complex then provide an estimate of the free energy of binding.

– Energy components are obtained from MC or MD simulations for inhibitors in water and for the protein-inhibitor complexes in water.

• Advantages– absolute free energies of binding can be approximated– only simulations at the endpoints of a mutation are required– computing demands are reduced to a few hours per compound.– Easy to treat structurally diverse ligands– Better accuracy


30ADMET in silico modeling towards Prediction Paradise?, Nature

SUMMARY

References• William L. Jorgensen, et al., The Many Roles of

Computation in Drug Discovery, Science 303, 1813 (2004);

• Han van de Waterbeemd and Eric Gifford, ADMET in silico modeling towards Prediction Paradise?, Nature reviews/drugdisc, March 2003;

• Wikipedia• The future of computation in drug discovery, By

Wavefunction on Monday, November 28, 2011• anchorquery.ccbb.pitt.edu/class/lecture.pdf• http://en.wikipedia.org/wiki/Pre-clinical_development• http://en.wikipedia.org/wiki/Clinical_trial• http://en.wikipedia.org/wiki/Drug_design#Computer-

aided_drug_design31

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The Many Roles of Computation in Drug Discovery