Computational Techniques in Support of Drug Discovery October 2, 2002 Jeffrey Wolbach, Ph. D.

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Sequential Drug Discovery  Many cycles of synthesis/testing to identify and optimize lead  Role of molecular modeling o unrealistic to jump from validated target to optimized lead o useful to reduce the number of synthesis/testing cycles o enables “first to file” o enlarge number of targets Choose Disease Lead Identification Lead Validation Lead Optimization ADME Candidate to Clinic Target Identification Target Validation

Choose a Disease Lead Identification Lead Validation Lead Optimization ADME Candidate to Clinic Target Identification Target Validation Drug Discovery in Parallel Knowledge-sharing environment: genomics, HTS, chemistry, ADME, toxicology Collect more data, on more compounds, more quickly Apply predictive models of “developability” early Enhanced understanding & predictive model building Increase share of patented time on market

Ligand-Based Design Ligand Structures w/Activities No Target Structure PharmacophoreAnalysis QSAR Database Searching New Candidate Structures for Synthesis/Testing Discern Similarities and Differences in Active Structures

Assume active molecules share a binding mode o Search for common chemical features of active molecules Don’t know binding mode, so active molecules are considered flexible o Search set of pre-determined conformers o Allow molecules to flex during search Pharmacophore Analysis Typical features: o H-Bond Donors o H-bond acceptors o Hydrophobic groups

Pharmacophore Models Chemical features in 3-D space Distance constraints between chemical features

QSAR Relates bioactivity differences to molecular structure differences Structure represented by numerical descriptors o Traditional (2D) QSAR o 3D QSAR - CoMFA Statistical techniques relate descriptors to activity  Activity  Descriptor Activity = D D D

QSAR - Traditional (2D) Descriptors are molecular properties o logP, dipole moment, connectivity indices... Structures + ActivityDescriptors logP = 1.9  = 2.8 E state = 7.2 logP = 2.1  = 3.5 E state = 5.5 logP = 1.7  = 2.3 E state = 6.7 Predictive Model (QSAR Equation) pK i =5.3 pK i =3.7 pK i =2.9 pK i =A + B(logP) + C(  ) + D(E state ) +... PLS MLR.

QSAR - 3D QSAR - CoMFA Comparative Molecular Field Analysis Descriptors are field strengths around molecules - electrostatic, steric, H-bond.. Fields can have easy physical interpretation pK i =A + B(D 1 ) + C(D 2 ) +...

QSAR/CoMFA - Interpretation High Coefficient (important) lattice points can be plotted around molecular structures

D Database Searching Searches often performed on bit-strings o “Fingerprints” (many types) o Fingerprints display neighborhood behavior Also includes substructure searching Can search for similarity or dissimilarity

Query is a collection of features in 3-D space o Pharmacophore o Lead compound / specific atomic groups 3D Database Searching Search a database of flexible, 3-D molecules o Molecules can’t be stored in every possible conformation o Allow molecules to flex to fit the query

Example of Structure-Based Design

Not restricted to ligand-based design Information about target can be included in the query o Can define steric hindrances o Additional interaction sites o Serves to filter hits from the search 3D Database Searching

Identification of Novel Matrix Metalloproteinase (MMP) Inhibitors A fibroblast collagenase-1 complexed with a diphenyl- ether sulphone-based hydroxamic acid MMPs Zinc-dependent proteases Involved in the degradation and remodeling of the extracellular matrix They are important therapeutic targets with indications in: Cancer Arthritis Autoimmunity Cardiovascular disease

Objectives Design high affinity MMP inhibitors based on the diketopiperazine scaffold by: Creating a virtual combinatorial library of candidate inhibitors Using virtual screening tools to identify candidates with the highest predicted affinity Perform R-group and binding mode analysis to guide library design

Synthesis of DKP-MMP inhibitors DKP-IDKP-II 1.) Esterification of the solid support (HO-) with an amino acid 2.) Reductive alkylation of the amino acid and acylation of the resulting secondary amine 3.) Deprotection of the N-alkylated dimer followed by cyclic cleavage from the resin yielding diketopiperazine (DKP)

Finding & Filtering Reagents UNITY 2D structure search of the ACD Filtered out: Metals MW > 400 RB > 15 Filtered out: Metals MW > 250 RB > 8 73 Boc protected amino acids1154 aldehydes

Selecting Reagents & Building the Virtual Library Selector™ Diverse selection of amino acids (R1) and aldehydes (R2) using: 2D Finger Prints Atom Pairs Hierarchical Clustering Legion™ Model the reaction and create virtual combinatorial library 55 amino acids(R1) x 95 aldehydes (R2) x 14 amino acids (R3) = 73,150 compounds(~75k, 8.5 MB) Randomly selected 14 amino acids for R3

The CombiFlexX Protocol Select a diverse subset of compounds using OptiSim Dock and score the compounds in the diverse subset using FlexX Select unique core placements using OptiSim Hold each core placement fixed in the binding site as each R-group is independently attached, docked, and scored. Sum the scores of the "R-cores" and subtract the score of the common core Computation times scale as the sum of the number of R- groups rather than as the product of the number of R-groups

Virtual Screening of DKP-MMP Inhibitors ~75k compound library MMP target structure collagenase-1 (966c.pdb) 150 diverse compounds selected and docked 39 non-redundant core placements based on RMSD > 1.5 Å.

Virtual Screening Results Docked 91% of the library 36 compounds/minute 331 compounds predicted to be more active than those published

Consensus Scoring Results Extracted the top 1000 library compounds based on Flex-X score Ranked the top library compounds and published “highly actives” using CScore 10 compounds predicted by all scoring functions to be more active than “highly actives”

Frequency of R-group use among 331 active virtual compounds R1 (55 reagents) R2 (95 reagents) R3 (14 reagents) R-Group Analysis in HiVol

R1 R-Group Analysis in HiVol, con’t. Frequency of R-group use among 331 active virtual compounds

Summary Used CombiFlexX and HiVol to: Identify highly promising candidates Perform R-group analysis & Binding mode analysis to guide further computational design of libraries Further Work Diversity/similarity analysis of the published and virtual libraries Use docking results for library design in Diverse Solutions SAR development

Binding Mode Analysis Frequency of core placement use

Frequency of R-group use among 331 active virtual compounds R1 (55 reagents) R2 (95 reagents) R3 (14 reagents) R-Group Analysis in HiVol