Mathematics In Drug Discovery: An Practitioner’s View Mathematics In Drug Discovery: An Practitioner’s View Dr. Jitao David Zhang, Bioinformatics and Computational Biology Dr. Jitao David Zhang, Bioinformatics and Computational Biology Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel Perlen Perlen -Kolloquium Kolloquium, , University of Basel, October 25th, 2018 University of Basel, October 25th, 2018
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• Now is the best time Now is the best time in in human human history to fight diseases history to fight diseases • Mathematics approaches are indispensable to modern drug discovery Mathematics approaches are indispensable to modern drug discovery • Interdisciplinary mathematics will transform drug discovery in the coming decades Interdisciplinary mathematics will transform drug discovery in the coming decades 3
The history of The history of Homo sapiens Homo sapiens is a history of living with, is a history of living with, understanding, and fighting diseases understanding, and fighting diseases Trypanosomes Nobel prize laureates 2018, A young patient of smallpox, Chloral hydrate, immune checkpoints, Plasmodium the first eradicated infectious disease the first synthesized drug and drugs targeting the pathways Personalized precise Personalized precise Hygiene, vaccination, Hygiene, vaccination, Tropical diseases Tropical diseases Pharmaceutical drugs Pharmaceutical drugs healthcare healthcare and antibiotics and antibiotics ~500,000 years ago ~150 years ago ~20 years ago ~250 years ago 4
Bioinformatics and computational biology, a branch of applied Bioinformatics and computational biology, a branch of applied mathematics mathematics , are , are indispensable indispensable for modern drug discovery for modern drug discovery Target proposal & assessment Mode-of-action (MoA) Knowledge- and Rational design Biomarker and data-driven target and safety profiling of of small molecules, translational hits/leads enabled by proposal and nucleotides and support omics technologies assessment antibodies Modified from Paul et al. “How to Improve R&D Productivity: The Pharmaceutical Industry’s Grand Challenge.” Nature Reviews Drug Discovery, 2010 Bioinformatics and computational biology Bioinformatics and computational biology have have become indispensable for modern drug discovery become indispensable for modern drug discovery 5
Applied mathematics empowers drug discovery by many ways Applied mathematics empowers drug discovery by many ways in drug discovery Applied mathematics is not a definable scientific field but a human attitude. Richard Courant (1888-1972) Statistics, Data Mining and Applied Combinatorics Stochastic Simulation Geometric Modeling Machine Learning and Graph Theory Molecular, Quantum, and Ordinary / Partial/ Stochastic Network Analysis Dynamical Systems Continuum Mechanics Differential Equations 6
Seemingly ‘pure’ mathematics significantly contributes to Seemingly ‘pure’ mathematics significantly contributes to understanding biology, too understanding biology, too The mathematician’s patterns, like the painter’s or the poet’s, must be beautiful Drug discoverer’s patterns, like the mathematician’s or painter’s or the poet’s, must be beautiful Godfrey Harold Hardy (1877-1947) It must be admitted that the biological examples … in the present paper are very limited. This can be ascribed … to the fact that biological phenomena are usually very complicated. … It is thought, however, that the imaginary biological systems … and the principles … should be of some help in interpreting real biological forms. The chemical basis of morphogenesis , 1952 Alan Mathison Turing (1912-1954) 7
Prerequisites to make a good drug that works Prerequisites to make a good drug that works Diagnosis • Potency Potency • Safety Safety Potency • Efficacy Efficacy • Diagnosis Diagnosis : doctors’ judgement + biomarkers Safety – Biomarkers are informative features derived from measurements of patient or patient material, e.g. blood chemistry, genetic make-up, imaging, etc. Efficacy • Other criteria: commercial rationale, development ability, intellectual property, etc. Success Success in in drug discovery is determined by potent, safe, efficacious drugs and accurate diagnosis drug discovery is determined by potent, safe, efficacious drugs and accurate diagnosis 8
The essence & THE challenge The essence & THE challenge of Drug Discovery of Drug Discovery Constrained optimization and decision making based on incomplete, Constrained optimization and decision making based on incomplete, noisy and heterogeneous data, and limited prior knowledge. noisy and heterogeneous data, and limited prior knowledge. 9
Bioinformatics and computational biology in preclinical Bioinformatics and computational biology in preclinical research research contribute contribute to to making safe and efficacious drugs making safe and efficacious drugs Safety Safety • Data mining reveals a network of early -response genes as a consensus signature of drug -induced in vitro and in vivo toxicity. Zhang et al., Journal of Pharmacogenomics, 2014. Efficacy Efficacy Efficacy Efficacy • Molecular phenotyping combines molecular information, biological relevance, and patient data Safety Safety to improve productivity of early drug discovery. Drawnel and Zhang et al. , Cell Chemical Biology, 2017. We do research in We do research in computational biology at computational biology at Roche and collaborate to Roche and collaborate to make safe make safe and efficacious drugs and efficacious drugs 10
One challenge in drug discovery: non One challenge in drug discovery: non -clinical safety assessment clinical safety assessment human in vivo animal in vivo • Limited in vitro-in vivo and cross- species translatability • Conflict between black-box prediction methods and the need to understand the mode of action human in vitro animal in vitro We need better (and interpretable) tools to predict safety profiles of drug candidates We need better (and interpretable) tools to predict safety profiles of drug candidates 11
Principles of gene expression profiling Principles of gene expression profiling Transcription Transcription Translation Translation DNA DNA RNA RNA Protein Protein Reverse Reverse DNA replication DNA replication transcription transcription Geneexpression profiling Differential Gene Expression Pathway/network analysis Figures: Wikimedia Commons/Thomas Shafee , CC/Adapted 12
TG-GATEs: TG GATEs: Toxico oxico genomics Project enomics Project - Genomics enomics Assisted ssisted Toxicity oxicity Evaluation valuation system ystem 170 Compounds • Japanese Consortium 2002 -2011 Japanese Consortium 2002 2011 • National Institute of Biomedical Innovation, National Institute of Health Sciences, and 15 > 2000 pharmaceutical companies, including Roche Chugai. • Data Data fully released in 2012 to fully released in 2012 to the public: the public: Time -series and dose -dependent experiments Cellular assays using 170 bioactive compounds • In vitro & in vivo gene expression profiling, each containing gene expression data of > 12000 about 20,000 genes • In vitro PicoGreen DNA quantification assay Pathology records • In vivo histopathology in liver and kidney > 24000 • In vivo clinical chemistry • Total raw data Total raw data size size >2 TB >2 TB Expression profiles TG TG-GATEs is a valuable data source to study drug GATEs is a valuable data source to study drug -induced toxicity induced toxicity in vitro in vitro and and in vivo in vivo 13
We built a computational We built a computational pipeline to identify early pipeline to identify early signatures signatures of of toxicity toxicity We integrate unsupervised learning, regression analysis, and network modelling to reach the goal We integrate unsupervised learning, regression analysis, and network modelling to reach the goal 14
Conserved dynamics of the early signatures in human and rat primary Conserved dynamics of the early signatures in human and rat primary hepatocytes is intrinsic to the network structure hepatocytes is intrinsic to the network structure Human Human Rat Rat • The network structure was constructed by queries in interaction database and literature information. • Boolean -network simulation (Nikolaos Berntenisand Martin Ebeling, BMC Bioinformatics 2013 ) suggests that the the Lines represent average inductions, and error bars indicate 95% confidence interval of the average induction. conserved dynamics of the network in human and rat conserved dynamics of the network in human and rat is encoded in the conserved structure of the network is encoded in the conserved structure of the network . Integrated data analysis reveals an evolutionarily conserved network with intrinsic dynamics Integrated data analysis reveals an evolutionarily conserved network with intrinsic dynamics that responds early to drug that responds early to drug -induced toxicity induced toxicity 15
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