Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods

PurposeWe propose a framework with simple proxies to dissect the relative energy contributions responsible for standard drug discovery binding activity.MethodsWe explore a rule of thumb using hydrogen-bond donors, hydrogen-bond acceptors and rotatable bonds as relative proxies for the thermodynamic terms. We apply this methodology to several datasets (e.g., multiple small molecules profiled against kinases, Mycobacterium tuberculosis (Mtb) high throughput screening (HTS) and structure based drug design (SBDD) derived compounds, and FDA approved drugs).ResultsWe found that Mtb active compounds developed through SBDD methods had statistically significantly larger PEnthalpy values than HTS derived compounds, suggesting these compounds had relatively more hydrogen bond donor and hydrogen bond acceptors compared to rotatable bonds. In recent FDA approved medicines we found that compounds identified via target-based approaches had a more balanced enthalpic relationship between these descriptors compared to compounds identified via phenotypic screensConclusionsAs it is common to experimentally optimize directly for total binding energy, these computational methods provide alternative calculations and approaches useful for compound optimization alongside other common metrics in available software and databases.

[1]  E. Freire Do enthalpy and entropy distinguish first in class from best in class? , 2008, Drug discovery today.

[2]  Martin Frank,et al.  Computation of Binding Energies Including Their Enthalpy and Entropy Components for Protein-Ligand Complexes Using Support Vector Machines , 2013, J. Chem. Inf. Model..

[3]  Brett A Tounge,et al.  Ligand efficiency and fragment-based drug discovery. , 2009, Drug discovery today.

[4]  Alex M. Clark,et al.  Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets , 2015, J. Chem. Inf. Model..

[5]  Mindy I. Davis,et al.  Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.

[6]  Bryan L Roth,et al.  Massively parallel screening of the receptorome. , 2008, Combinatorial chemistry & high throughput screening.

[7]  Christopher P Austin,et al.  Three classes of glucocerebrosidase inhibitors identified by quantitative high-throughput screening are chaperone leads for Gaucher disease , 2007, Proceedings of the National Academy of Sciences.

[8]  Ian A. Watson,et al.  Rules for identifying potentially reactive or promiscuous compounds. , 2012, Journal of medicinal chemistry.

[9]  T. Blundell,et al.  Structural biology in fragment-based drug design. , 2010, Current opinion in structural biology.

[10]  Markus A Lill,et al.  Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR , 2006, ChemMedChem.

[11]  Tao Xu,et al.  Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis , 2014, J. Chem. Inf. Model..

[12]  Garland R. Marshall,et al.  PHOENIX: A Scoring Function for Affinity Prediction Derived Using High-Resolution Crystal Structures and Calorimetry Measurements , 2011, J. Chem. Inf. Model..

[13]  Hongwei Guo,et al.  A Simple Algorithm for Fitting a Gaussian Function , 2012 .

[14]  Irene Luque,et al.  Structural parameterization of the binding enthalpy of small ligands , 2002, Proteins.

[15]  Gastone Gilli,et al.  Enthalpy-entropy compensation in drug-receptor binding , 1994 .

[16]  Sean Ekins,et al.  Evolving molecules using multi-objective optimization: applying to ADME/Tox. , 2010, Drug discovery today.

[17]  S. Bembenek,et al.  Ligand binding efficiency: trends, physical basis, and implications. , 2008, Journal of medicinal chemistry.

[18]  Rongshi Li,et al.  Kinase Inhibitor Drugs , 2009 .

[19]  R. Reynolds,et al.  High Throughput Screening for Inhibitors of Mycobacterium tuberculosis H 37 Rv , 2012 .

[20]  N. Meanwell Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. , 2011, Chemical research in toxicology.

[21]  R. Ellson,et al.  Gradient, Contact-Free Volume Transfers Minimize Compound Loss in Dose-Response Experiments , 2010, Journal of biomolecular screening.

[22]  Ajay,et al.  Recognizing molecules with drug-like properties. , 1999, Current opinion in chemical biology.

[23]  Wei Xu,et al.  In silico derived small molecules bind the filovirus VP35 protein and inhibit its polymerase cofactor activity. , 2014, Journal of molecular biology.

[24]  Sean Ekins,et al.  A collaborative database and computational models for tuberculosis drug discovery. , 2010, Molecular bioSystems.

[25]  Jeffrey R. Huth,et al.  Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups , 2007, J. Comput. Aided Mol. Des..

[26]  D. Mobley,et al.  Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. , 2013, Annual review of biophysics.

[27]  Gerhard Klebe,et al.  The Use of Thermodynamic and Kinetic Data in Drug Discovery: Decisive Insight or Increasing the Puzzlement? , 2015, ChemMedChem.

[28]  Osmar Norberto de Souza,et al.  Discovery of New Inhibitors of Mycobacterium tuberculosis InhA Enzyme Using Virtual Screening and a 3D-Pharmacophore-Based Approach , 2013, J. Chem. Inf. Model..

[29]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[30]  James Inglese,et al.  Reporting data from high-throughput screening of small-molecule libraries. , 2007, Nature chemical biology.

[31]  György G Ferenczy,et al.  Thermodynamics guided lead discovery and optimization. , 2010, Drug discovery today.

[32]  Lynn Rasmussen,et al.  Antituberculosis activity of the molecular libraries screening center network library. , 2009, Tuberculosis.

[33]  G. Keserű,et al.  Is there a link between selectivity and binding thermodynamics profiles? , 2015, Drug discovery today.

[34]  E. Freire THERMODYNAMICS IN DRUG DESIGN. HIGH AFFINITY AND SELECTIVITY , 2005 .

[35]  P. Hajduk,et al.  Navigating the kinome. , 2011, Nature chemical biology.

[36]  Lynn Rasmussen,et al.  High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv. , 2012, Tuberculosis.

[37]  Tina Ritschel,et al.  Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery , 2018 .

[38]  D. Swinney,et al.  How were new medicines discovered? , 2011, Nature Reviews Drug Discovery.

[39]  William N. Hunter,et al.  Structure-based Ligand Design and the Promise Held for Antiprotozoan Drug Discovery* , 2009, Journal of Biological Chemistry.

[40]  John P. Overington,et al.  The ChEMBL database: a taster for medicinal chemists. , 2014, Future medicinal chemistry.

[41]  L. Amzel,et al.  Compensating Enthalpic and Entropic Changes Hinder Binding Affinity Optimization , 2007, Chemical biology & drug design.

[42]  Alexander Hillisch,et al.  Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. , 2006, Drug discovery today.

[43]  R. Guha,et al.  Genome Editing-Enabled HTS Assays Expand Drug Target Pathways for Charcot–Marie–Tooth Disease , 2014, ACS chemical biology.

[44]  N. Cohen,et al.  Structure-based drug design and the discovery of aliskiren (Tekturna): perseverance and creativity to overcome a R&D pipeline challenge. , 2007, Chemical biology & drug design.

[45]  Conor R. Caffrey,et al.  Drug Discovery for Schistosomiasis: Hit and Lead Compounds Identified in a Library of Known Drugs by Medium-Throughput Phenotypic Screening , 2009, PLoS neglected tropical diseases.

[46]  Stewart T. Cole,et al.  High Content Screening Identifies Decaprenyl-Phosphoribose 2′ Epimerase as a Target for Intracellular Antimycobacterial Inhibitors , 2009, PLoS pathogens.

[47]  Antony J. Williams,et al.  Dispensing Processes Impact Apparent Biological Activity as Determined by Computational and Statistical Analyses , 2013, PloS one.

[48]  Daniel A Erlanson,et al.  Learning from our mistakes: the 'unknown knowns' in fragment screening. , 2013, Bioorganic & medicinal chemistry letters.

[49]  G. Klebe Applying thermodynamic profiling in lead finding and optimization , 2015, Nature Reviews Drug Discovery.

[50]  Ryan T. Strachan,et al.  Screening the receptorome: an efficient approach for drug discovery and target validation. , 2006, Drug discovery today.

[51]  Yi-Ping Phoebe Chen,et al.  Structure-based drug design to augment hit discovery. , 2011, Drug discovery today.

[52]  A Brigo,et al.  Discovery of HIV-1 integrase inhibitors through a novel combination of ligand and structure-based drug design. , 2005, Medicinal chemistry (Shariqah (United Arab Emirates)).

[53]  O. Ichihara,et al.  The Importance of Hydration Thermodynamics in Fragment‐to‐Lead Optimization , 2014, ChemMedChem.

[54]  James Inglese,et al.  Identification of drug modulators targeting gene-dosage disease CMT1A. , 2012, ACS chemical biology.

[55]  J. Baell,et al.  Chemistry: Chemical con artists foil drug discovery , 2014, Nature.

[56]  Lynn Rasmussen,et al.  High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. , 2009, Tuberculosis.

[57]  Michael D Shultz,et al.  Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters. , 2013, Bioorganic & medicinal chemistry letters.

[58]  Hongwei Guo,et al.  A Simple Algorithm for Fitting a Gaussian Function [DSP Tips and Tricks] , 2011, IEEE Signal Processing Magazine.

[59]  D. Bojanic,et al.  Impact of high-throughput screening in biomedical research , 2011, Nature Reviews Drug Discovery.

[60]  Alfonso Mendoza,et al.  Fueling Open-Source Drug Discovery: 177 Small-Molecule Leads against Tuberculosis , 2013, ChemMedChem.

[61]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[62]  Celia A Schiffer,et al.  Extreme entropy-enthalpy compensation in a drug-resistant variant of HIV-1 protease. , 2012, ACS chemical biology.

[63]  E. Freire,et al.  A Thermodynamic Approach to the Affinity Optimization of Drug Candidates , 2009, Chemical biology & drug design.

[64]  George M Whitesides,et al.  Designing ligands to bind proteins , 2005, Quarterly Reviews of Biophysics.

[65]  C. Fishwick,et al.  Applications of structure-based design to antibacterial drug discovery. , 2014, Bioorganic chemistry.

[66]  Sean Ekins,et al.  Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery , 2013, Pharmaceutical Research.

[67]  Andrei Leitão,et al.  Ligand efficiency metrics considered harmful , 2014, Journal of Computer-Aided Molecular Design.

[68]  T. Willson,et al.  Seeding Collaborations to Advance Kinase Science with the GSK Published Kinase Inhibitor Set (PKIS) , 2014, Current topics in medicinal chemistry.

[69]  Irwin D Kuntz,et al.  Stability of macromolecular complexes , 2002, Proteins.