DRUG DEVELOPMENT RESEARCH 72 : 66–73 (2011)

Research Overview

Mechanistic Disease Modeling as a Useful Toolfor Improving CNS Drug Research and Development

Hugo Geerts1,2�1In Silico Biosciences, Berwyn, Pennsylvania

2University of Pennsylvania, School of Medicine, Philadelphia, Pennsylvania

Strategy, Management and Health Policy

Enabling

Technology,

Genomics,

Proteomics

Preclinical

Research

Preclinical Development

Toxicology, Formulation

Drug Delivery,

Pharmacokinetics

Clinical Development

Phases I-III

Regulatory, Quality,

Manufacturing

Postmarketing

Phase IV

ABSTRACT Despite tremendous advances in the basic understanding of CNS diseases, successfulclinical drug development in psychiatry and neurology has been limited, putting huge pressure onremaining CNS R&D programs in pharmaceutical companies. In this report, it is proposed thatre-engineering parts of the pharmaceutical Research and Development process by integrating complexmodeling and simulation approaches, similar to the aerospace and micro-electronics industry, has thepotential to increase the clinical predictability of animal models and to reduce the attrition rate in clinicaldrug development. This report will present top-down Mechanistic Disease Modeling approaches inrelation to bottom-up Systems Biology with specific emphasis on CNS drug R&D. Both combine basicresearch data with human clinical outcome, but in contrast to System Biology that generically modelsintracellular pathways and protein-protein networks, Mechanistic Disease Modeling models the emergentproperties of neuronal cell firing activity in large interacting neuronal networks. Such an outcome is muchcloser to physiological and behavioral processes that drive actual clinical scales. Also illustrated here aresome practical applications in the area of Alzheimer’s disease and schizophrenia for CNS Research andDevelopment, such as guiding multitarget drug discovery, evaluating both the harmful and beneficial off-target human effects of candidate drugs, as well as exploring the effect of co-medications and functionalgenotypes on the candidate drug efficacy and sensitivity analysis for responder identification. Drug DevRes 72:66–73, 2011. r 2010 Wiley-Liss, Inc.

Key words: simulation; drug discovery; systems biology; disease modeling

THE NEED FOR A NEW DRUG DISCOVERYAND DEVELOPMENT APPROACH

There is increasing evidence that Drug Researchand Development in general [Grabowski, 2004] and inCNS disorders in particular has an inadequate successrate to sustain the development of new successfuldrugs. Only 8% of all drugs entering Phase I trials forCNS disorders reaches the market [Kola and Landis,2004]. This is in part due to the lack of predictabilityof animal models and our incomplete understandingof the human complex brain neurophysiology andpathology. Some of the underestimated limitations ofanimal models, such as (1) differences in neurotransmitter

circuitry and drug metabolism, (2) the incompleterepresentation of the full human pathology, (3) theabsence of important functional genotypes in animalmodels, (4) the pharmacodynamic interference of allowedco-medications, and (5) the difference in drug affinities

DDR

Published online in Wiley Online Library (wileyonlinelibrary.com).DOI: 10.1002/ddr.20403

Hugo Geerts is an employee of In Silico Biosciences.

�Correspondence to: Hugo Geerts, In Silico Biosciences,686 Westwind Drive, Berwyn, PA 19312.E-mail: Hugo-Geerts@In-Silico-Biosciences.com

�c 2010 Wiley-Liss, Inc.

between rat and human subtype receptors have beenaddressed in a previous review [Geerts, 2009].

For all neurodegenerative diseases, only 12 newcompounds have been approved by the FDA inthe decade between 2000 and 2009. For example, thecurrently approved medications for Alzheimer’s diseasehad their therapeutic rationale identified in the late1970s [Davies and Maloney, 1976], and a clinical proof-of-concept was achieved 15 years later by tacrine, thefirst acetylcholinesterase inhibitor (AChE-I). Despitethe tremendous investment in Alzheimer research,since the identification of the Ab peptide as the majordriver of the amyloid pathology [Glenner, 1988], morethan 20 years have already elapsed without even aclinical proof of concept.

These examples highlight the difficulty of devel-oping new drugs in the CNS area. The problems withsuccessful clinical development programs pose asubstantial treat to the business model of thepharmaceutical industry to the point that pharmacompanies are exiting the CNS field, a problemrecognized by the NIMH [Brady et al., 2009].

Other industries, such as aerospace and micro-electronics have successfully dealt with a high degree ofcomplexity by engineering their Research and Deve-lopment process using advanced modeling and simula-tion techniques, even in the presence of incompleteinformation. Although the knowledge is more limited inbiology, we will argue in this report that broaderacceptance of computer modeling and simulation at allpoints in Pharmaceutical Research and Developmenthas the capacity to improve the success rate of CNSprojects. Recent studies suggest that there are evensimilarities in the topology of brain networks with verylarge-scale integrated circuits [Bassett et al., 2010].

A pledge for a national consortium to improve insilico modeling for Alzheimer’s Disease has beenadvanced recently [Khatchaturian and Lombardo,2009]. The idea is to pool the academic expertise inmodeling with the aim of increasing the functionalunderstanding of the biology.

This report presents an overview of MechanisticDisease Modeling in CNS Drug Research and Deve-lopment with a special emphasis on similarities andcomplementarities of Systems Biology.

SYSTEMS BIOLOGY AND MECHANISTIC DISEASEMODELING APPROACHES USED IN CNS DRUG

RESEARCH AND DEVELOPMENT

Systems Biology aims to transform informationfrom large (experimental) datasets such as micro-arrayexperiments into actionable knowledge, using advan-ced statistical and mathematical techniques. Thisgeneric approach is disease-independent and the same

techniques can be applied to a large variety ofproblems. Its greatest success has been in supportingthe understanding of intracellular pathways involved incancer biology and its application in drug R&D.

It is worthwhile to mention a few academicinitiatives focused on Alzheimer’s disease. ‘‘Chalk-board’’ is a systems analysis approach around qualita-tive identification of intracellular pathways that hasbeen applied to APP proteolysis [Cook et al., 2007],while a pathway analysis using a statistical data-miningtechnique on publicly available protein databases[Chen et al., 2006] correctly identifies key proteins inAlzheimer pathological networks. However interestingthese approaches might be for understanding ADpathology better and identifying new possible targets,to our knowledge they have not been used in actualCNS drug Research and Development.

Mechanistic Disease Modeling in CNS diseasesessentially simulates the emergent activity properties ofbiophysically realistic neuronal circuits in well-definedphysiological readouts (such as imaging and EEG) andadjusts the parameters to fit experimental observationsin a training set. Due to the specific nature of thepharmaceutical drug process, relevant physiologicalprocesses are simulated at various levels of details(Fig. 1), from the competition between drugs and

Fig. 1. Mechanistic disease modeling can be performed on manydifferent levels. The most detailed model (1) is a generic receptorcompetition model that simulates the competition between differentagents for the same receptor binding site and can be used fordetermining the functional drug brain concentration from PETdisplacement imaging studies. A spatio-temporal cholinergic synapse(2) describes the full state-description of the pre- and postsynapticreceptors for determination of the dose-response of nicotinic Achreceptor modulators while more complex neuronal models (3) capturea comprehensive range of spatio-temporal interactions modulatingexcitatory behavior. Finally, complex biophysical networks (4)simulate firing activity and its emergent properties in a more realisticnetwork of different neuronal cell types. Some of these models will bedescribed in more detail in the text.

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neurotransmitters for the same receptor to their effecton the properties of large networks using computa-tional neuroscience approaches.

The field started with the famous Hodgkin andHuxley simulations [Hodgkin and Huxley, 1952] ofaction potentials and has evolved substantially thanks tomajor advances in computer hardware and software.The term dynamical neuropharmacology was firstcoined by Erdi and colleagues [2006] and refers tothe pharmacological modulation of brain activityoscillations, its computer simulation and experimentalverification.

Interestingly, a number of academic in silicomechanistic models of Alzheimer’s disease have beenpublished. A circuit model was used to evaluate effectsof run-away synaptic modulation and progressionof AD neuropathology over various brain regions[Hasselmo, 1997]. In another study, Alzheimerpathology was introduced as a cholinergic deficit ina biophysically realistic network model of the hippo-campus [Menschik and Finkel, 1999], leading toa decreased frequency of intrinsically generatedg-oscillations and to a lower time for the attractorpattern to settle, thereby decreasing the probability formemory retrieval. A spatiotemporal computer modelof neuro-inflammatory interaction between plaques,microglia, and neurons [Edelstein-Keshet and Spiros,2002] allowed evaluating the contributions of differentcytokines to neuronal cell death. Using recent insightsof ‘‘small-world’’ networks in human brain, computa-tional modelings suggest that networks can significantlyreduce the effects of neuronal cell death [Rubinovet al., 2009] by activity-dependent plasticity andrelatively small degree of neurogenesis. The IBM-sponsored Blue Brain project [Markram, 2006] simu-lates a neocortical column consisting of 10,000 3Ddigitizations of real neurons that are populated withmodel ion channels constrained by the genetic makeupof over 200 different types of neurons.

It is important to make a distinction betweenbiophysically realistic models and more abstract con-nectionist models [Adeli et al., 2005]. For example, thetemporal difference learning model of dopaminemodulation, for instance, describes temporal changesbetween activity of abstract ‘‘agents’’ that correspond tolarge neuronal populations and has been clinicallyvalidated [Suri, 2002]. However interesting thesemodels might be for understanding the interactionsbetween brain regions, they are not sufficiently detailedto simulate actual pharmacological processes.

A mechanistic disease simulation approach withthe purpose to solve specific questions along thecontinuum of drug research and development hasbeen industrialized by companies such as Entelos

(www.entelos.com) in metabolism and inflammation,and Optimata (www.optimata.com) and Merrimack (www.merrimack.com) in oncology and In Silico Biosciencesin CNS disorders (www.in-silico-biosciences.com).

MAKING MECHANISTIC DISEASE MODELINGACTIONABLE FOR R&D

In order to make computational neuroscienceuseful for CNS Drug Research and Development, fourmajor expansions need to be implemented. As anexample, we illustrate these four steps for thedevelopment of a clinically relevant computationalworking memory model.

The basic computational model consists of multi-compartment versions of 100 pyramidal neurons and40 interneurons with different types of Na1, K1, andCa11 ion-channels [Durstewitz et al., 2000] and hasbeen derived and calibrated from actual experimentaldata in single neurons in the primate cortex duringworking memory tasks [Williams and Goldman-Rakic,1995]. The model parameters are adjusted so that firingactivity reflects the experimental observations in theprimate cortex after a cue is presented in anexperimental working memory task. Such an experi-mental cue is simulated as the injection of a singlecurrent in the in silico model. A state diagram (seeFig. 2 for an example), in which each dot on a linerepresents the action potentials of an individualsimulated neuron, can then be generated to determinethe working memory time span, a time period in whichthe system is capable of holding a specific patternin the face of a distractor pattern.

The first addition introduces CNS targets such asG protein–coupled receptors or transporters, whichare primary targets for CNS drugs action. Targetphysiology is introduced by implementing the relation-ship from observed experimental data from in vivo or invitro (preclinical) experiments. For instance, dopaminestimulates D1 receptors leading to an increase ofNMDA receptor density [Kita et al., 1999], a reductionof AMPA currents [Law-Tho et al., 1994] and Ca21

current [Brown et al., 1993], a facilitation of Na1

current [Yang and Seamans, 1996], and GABA currents[Zhou and Hablitz, 1999] with appropriate temporalrelationships. In a similar way, the physiology of manyother targets that are affected by CNS active drugs,such as D2, D4, 5-HT1A, 5-HT2A, 5-HT3, a4b2 anda7 nicotinic Ach receptors, M1 and M2 muscarinic Achreceptors, can be implemented.

The second addition determines the activationlevels of these receptors in normal conditions and theirchanges as a consequence of either pathologicalconditions, specific genotypes, or after drug treatment.

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Some of this information can be derived from humanPET displacement imaging studies.

For example, Catechol-O-Methyl Transferase(COMT) breaks down cortical dopamine and norepi-nephrine and comes in three genotypes with theVal158Val genotype having the fastest enzyme activityand the lowest free dopamine level [Doyle and Yager,2008]. PET imaging studies in subjects revealedgenotype-specific differences in the cortical bindingpotential of NNC112, a specific D1R radiotracer[Slifstein et al., 2008]. Using a computational synapsemodel that simulates the competition between dopa-mine and the radiotracer, quantitative differences inthe half-life of free DA at cortical synapses can bematched to the clinical observations and allows the insilico model to be ‘‘personalized’’ to the individualCOMT genotype [Geerts, 2009]. This genotype isabsent in rodents, so that it is impossible to predictthese effects from a preclinical rodent animal model.

This particular simulation approach can also beused to determine the functional concentration of CNSactive drugs at specific doses or plasma levels fromtheir effect in human PET tracer imaging experiments,i.e., striatal raclopride displacement at the D2R forneuroleptics [Elsinga et al., 2006] or levels of AChEinhibition for currently approved Alzheimer’s drugs[Kadir et al., 2008].

The third aspect deals with implementation of theCNS pathology in this in silico model. Alzheimer’s

disease pathology is introduced by random synapsedeletion on a fraction of pyramidal neurons and adecrease in cholinergic tone [Dickstein et al., 2007],matched to post-mortem studies on the decrease indendritic arborisation, synapse loss, and cholinergicdysfunction. Implementing these pathological changesleads to a significant decrease in performance in theworking memory model.

The fourth addition is the calibration of the insilico model with actual human clinical data. Byimplementing the different conditions of actual clinicalstudies (placebo, drug treatment, different genotypes)in the in silico model, changes in the outcome (i.e.,working memory span) can then be compared to theobserved clinical readouts (i.e., the number of errors ina 2-Back working memory test) [Weickert et al., 2004;Bertolino et al., 2004; Green et al., 2005]. The modelparameters (i.e., the coupling between receptor activa-tion and their effects on ion-channels) are tuned tooptimize the correlation [Geerts et al., 2006]. Thequality of such a calibration can be assessed bypredicting the outcome of a test-set and can giveconfidence on any prospective prediction with a novelinvestigative compound or combination of interven-tions. Because this procedure involves a large numberof drugs at different doses (including negative out-comes), this in silico model validation is much morestringent than validation of traditional preclinicalanimal models, where typically the reduction of the

Fig. 2. The output of the working memory network as a ‘‘state-diagram.’’ Each line represents the activity of a single neuron and each dot is anaction potential. At 2,000 msec, injection of a current leads to an activation of the 10 neurons in the ‘‘attractor’’ pattern. This activation pattern isactive for a certain time without further stimulation, representing a ‘‘working memory’’ process, until increased activity at the 10 distractorneurons drives the system out of the attractor state and the memory trace is lost. The duration of the working-memory span (horizontal line)corresponds to realistic values in human or primate trials.

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phenotypic deficit with one or two ‘‘reference’’compounds at an arbitrary dose is often sufficient forvalidation.

APPLICATIONS OF MECHANISTIC DISEASEMODELING IN CNS DRUG RESEARCH

AND DEVELOPMENT

Support of the Medicinal Chemistry Campaign in aMulti-Target Drug Discovery Project

Recently multi-targets have been suggested tobetter address global network imbalances [Bassett andBullmore, 2009] in CNS diseases such as Alzheimer’sdisease [Buccafusco, 2009] or psychiatric disorders[Wong et al., 2008]. Such an approach has not beenwidely used, because the criteria for compoundselection to guide chemistry efforts based on anaverage of the affinity values from binding assays willprobably lead to errors [Wong et al., 2008], as thecandidate compounds have to compete with differentendogenous neurotransmitters having completely dif-ferent affinities for their receptors.

When introducing the primary pharmacology ofall different candidate hits in a Mechanistic Modelingand Simulation platform, they can be ranked very rapidlyaccording to their anticipated effect on the neuronaldynamics in the full network model. Having all molecularprofiles pass through this process virtually eliminates thepossibility of false negatives, i.e., active compounds thatare not selected for further development.

Off-Target Effects of Candidate Molecules

The off-target effects of candidate compounds areoften described as ratios in affinities, and values greaterthan 500–1,000 are deemed safe. However, theendogenous neurotransmitter at the off-target receptormight have a lower affinity for its target than theneurotransmitter or substrate for the primary target.For example, a relatively weak Dopamine D1Rantagonism as an off-target effect in a putativeAlzheimer medication might reduce working memoryto the same degree as the effect of the functionalCOMT genotype (see above), which is clinicallydetectable [Geerts et al., 2006; Weickert et al., 2004;Bertolino et al., 2004]. In this context, it is of interest tonote that dimebon (latrepirdine), a compound thatrecently failed in a large Phase III trial in Alzheimer’sdisease, had an affinity of about 600 nM for the humanD1R [Okun et al., 2009] while animal studies show thatbrain levels of 170 nM and higher can easily be attained[Giorgetti et al., 2010].

Mechanistic Modeling and Simulation permitsestimating off-target effects of the putative leadcandidates in an early stage of Drug Discovery based

upon the human receptor pharmacology of the activemoiety and as such has the capability to guidemedicinal chemistry efforts.

Difference in Pharmacology Between Rodentand Human Receptor Subtypes

Because traditional drug discovery is based onpreclinical animal models, the drug candidate is usuallyoptimized for the total rodent pharmacology but notnecessarily for the human receptor pharmacology.About 8% of the drugs from a publicly availabledatabase (http://pdsp.med.unc.edu/indexR.html) havea sufficiently different affinity for the human ascompared to the rodent receptor subtype that can leadto clinically observable differences [Geerts, unpub-lished data].

By using only human receptor pharmacology andhumanized hybrid brain circuit model, MechanisticModeling and Simulation can assess the effect of suchdifferences. This avoids developing a drug candidatewith an optimal receptor profile for rodents but animperfect or even inactive profile for the humanreceptors.

Full Dose-Response of the Candidate Drug

An inverse U-shape response is unfortunatelymore often the rule rather than the exception and theMaximum Tolerable Dose determined in a Phase Istudy is not always the optimal dose. For example, thedifferential dose-response between donepezil andgalantamine [Woodruff-Pak et al., 2002] was modeledusing a spatio-temporal computer model of thecholinergic synapse and the inverse U-shape dose-responses were found to differ substantially betweenthese compounds [Geerts et al., 2002], a result that waslater confirmed experimentally [Zhang et al., 2004] andhad substantial implications for in vivo dosing [Geertset al., 2005].

If the inverse U-shape dose-response is caused byoff-target effects of the investigative drugs at higherconcentrations, simulation could be used to helpidentify better back-up compounds. If the inverseU-shape dose-response is caused by the endogenousphysiology of the target, as is the case with ligand-basedion channels, simulation could help identify theoptimal dose.

Co-Medications in Clinical Trials

Co-medications are rarely studied in preclinicalanimal models but in clinical trials they can interferewith the primary pharmacology of the investigativedrug. For example, smoking might significantly alterthe dynamics of agents acting at nicotinic Ach receptorsto the point that they completely saturate a4b2 nAChR

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[Brody et al., 2006]. Similarly, the complex pharmacol-ogy of marketed antipsychotics on various receptorscan interfere with the effects of cognitive enhancerssuch as alpha7 nAChR or GlyT1 inhibitors aimed atimproving cognitive deficits in schizophrenia [Geertset al., 2006]. This has implications for augmentationtherapy trials.

Failure to account for these effects can reduce thesignal in clinical trials. Modeling the interaction betweentwo or more drugs at the appropriate brain concentra-tions in a network model could possibly improve clinicaltrial design using appropriate stratification.

Genotype Effects in Clinical Trial

The variability of clinical response in trials ispartly due to different functional genotypes in thepatient population. Some of the functional humangenotypes might interfere with the intended pharma-cology of the investigative compound and in such casesappropriate stratification is advised [Geerts, 2009].

For example, the well-characterized COMTVal158Met genotype can have dramatic effects onhuman cognition through its effect on dopamine andnorepinephrine breakdown in the cortex [Bilder et al.,2004] and has relevance for all drugs that directly orindirectly act on catecholamines.

The working memory model presented above[Geerts, 2009] has been calibrated for the threedifferent genotypes. Our simulations suggest that thisgenotype can interfere with the pharmacological effectof many investigative drugs and that, therefore,appropriate stratification is needed to ensure well-balanced treatment arms [Hariri, 2009]. As most of thehuman functional genotypes are absent in animalmodels, a properly validated mechanistic diseasemodeling approach is the only way to estimate theeffects of this genotype variability in the clinical trials.

Responder Profiles

Understanding the biology of responder profiles isof interest both with regard to the concept of targetedtherapies and differentiation with existing therapies. Bykeeping the pharmacological profile of the drugconstant and systematically changing the biologicalcouplings in the in silico model so as to reproduce theobserved clinical response for the drug, we can identifythe biological pathways that drive the unique clinicalresponse to the drug under investigation.

This can clarify the underlying biology of thepharmacogenomic results of responder populations. Asan example, the biology of iloperidone clinical respon-ders in schizophrenia was studied both with apharmacogenomic approach [Lavedan et al., 2009]

and the mechanistic modeling approach [Geertset al., 2010].

SYSTEMS BIOLOGY AND MECHANISTIC DISEASEMODELING: TWO SIDES OF THE SAME COIN?

Systems Biology as a community has been moreeffective at addressing concrete questions in pharma-ceutical research and development [De Schutter,2008], because they can extract actionable informationfrom changes in large –omics datasets, studied inexperimental systems, such as DNA samples, tumorbiopsies, or body fluids. By using advanced statisticaltechniques and inference analysis to derive complexrelationships, specific questions such as relatingbiomarkers to clinical outcomes or identifying thepathways where certain drugs act can be addressed.Systems Biology approaches of CNS diseases haverelied upon genomic data or proteomic biomarkersfrom plasma or CSF, but this does not lend itself easilyto the complex activity of large neuronal networks inthe human brain.

In contrast, Mechanistic Disease Modeling is bydefinition more limited in scope because it is basedupon the integration of limited published informationin a top-down quantitative framework. The approachobviously does not have the same molecular detail asSystems Biology and, therefore, it is mandatory that itis validated using the correlation between modeloutcomes and clinical outcomes in both a retro-activeand prospective way.

In CNS diseases, large networks of interactingbrain regions and neurotransmitter circuits determinethe change of neuropathology and the outcome oftreatment interventions [Bassett and Bullmore, 2009]and recent advances in network modeling from BOLDfMRI imaging studies, such as Structural Equationmodeling [Marco et al., 2009] or Granger causality-based statistical approaches [Demirci et al., 2009],provide a sound experimental basis to document thosenetwork relationships. Computational neurosciences,i.e., the ability to study emergent properties of largeneuronal networks and simulating the informationprocessing of brain activity, is well adapted to addressthis complexity.

Mechanistic Disease Modeling starts where CNSSystems Biology ends. Systems Biology, a bottom-upapproach focused on intracellular pathways can providethe framework for coupling intracellular events tochanges in membrane receptors and ion-channels,which in turn form the basis of large-scale simulationof electrical brain activity, that are more easilysimulated in Mechanistic Disease Modeling. Table 1summarizes differences and similarities between thesetwo different modeling approaches.

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CONCLUSION

This report provides arguments that MechanisticDisease Modeling of relevant CNS neurotransmittercircuits is well suited to address the specific issuesaround CNS Drug Research and Development. It addstranslational processes such as receptor physiology,drug-receptor interaction, pathology, and clinical vali-dation to the vast academic expertise in computationalneuroscience. We have illustrated a number of veryspecific and concrete questions that arise during CNSDrug Discovery and Development.

As a top-down modeling approach, MechanisticDisease Modeling by definition is inherently limited in itsmolecular detail, but the outcome is closer to actualimaging modalities or behavioral outcomes. In addition,the approach uses the language and terminology com-monly used in neurology communities and will likely helpstructuring the available preclinical and clinical informa-tion into a coherent and actionable framework that aims toget better medicines faster to the right patients.

ACKNOWLEDGMENTS

This report is dedicated to the memory ofProfessor Leif Finkel (University of Pennsylvania)whose contributions to the field of computationalneuropharmacology inspired many of us.

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TABLE 1. Conceptual Differences and Similarities Between Systems Biology and Mechanistic Modeling in CNS

Process Systems biology Mechanistic modeling

Input Large –omics datasets Detailed biological knowledgeOutput Topographic networks and qualitative relationship Quantitative dynamical readout changesApproach Statistical techniques Differential equations and stochastic simulationsScope Very broad, more complete Limited and focusedAdvantages Better suited for intracellular networks Physiological readout closer to actual brain imaging

modalitiesDisadvantages Less quantitative, more difficult to interpret relation

with clinical outcomeNeed detailed data on physiology and pathology;

not always availableCausality Limited, hard to implement Naturally present because of temporal link

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