Is AI the Holy Grail for Drug Discovery? Using AI and In Silico Methods to Design Drugs

Transcription

1 Is AI the oly Grail for Drug Discovery? Using AI and In Silico Methods to Design Drugs Ed Addison, CEO Phone: (910)

2 WY USE AI & WERE WE ARE USIG IT

3 Can we improve this picture with AI? $2.6B = Total Spend / #Approved Drugs Can AI Reduce Cost? Includes all failures too many! Can AI Reduce the failure rate? Too many compounds are tested! Can AI produce drug-like molecules de novo to eliminate hit or miss?

4 TS is the Emperor With o Clothes! Millions of $$ spent on TS, but it did not save the world, and still requires combining science with luck, resulting in throwing things against the wall to see what sticks. TS actually drives the $2.6B cost of drug development.

5 55 Ways that Cloud Pharmaceuticals is Using AI Deep Other Traditional Augmented Learning Machine AI Intelligence Learning Toxicology Blood Brain Medicinal Enhance Barrier Filter Chemistry What We Know Evaluation Binding Affinity Equation

6 66 A OVERVIEW OF OUR PROCESS

7 77 Cloud Pharmaceuticals Cloud-based Drug Design Platform Cloud Pharmaceuticals designs completely IP-clear, novel molecules that have outstanding drug-like qualities, low toxicity, can be synthesized, and are biologically active We design out much of the risk of In Silico Platform later-stage clinical failure ovel Drugs

8 88 Core Engine Components: Quantum Molecular Design (QMD) QMD is Cloud Pharmaceuticals proprietary system to implement a comprehensive data analytics suite to design a novel molecular space and find novel small molecules as drug candidates AI Use expert system to design vast and novel virtual molecular space and AI to search it Binding Energy Accurate computational chemistry techniques Properties Smart filter technology

9 99 Screening vs. Design SCREEIG merely replaces TS (only if its good) Known molecules To get IP requires SAR work Inhibitors or agonists, but not drugs Synthesis guaranteed Screening decks and databases are fixed and repeatedly reused DESIG finds novel molecules ew starting points ovel compounds IP generally available Molecules are drug like Synthesizeable if designed properly design gives synthetic chemists a pathway SAR may or may not be needed iterative design Computer hours Brute Force = exponentially expensive Inverse Design System Size

10 10 Artificial Intelligence Search Technology Proprietary proven AI search of vast molecular space for novel drug candidates 1 st step: Automatically designs targeted, novel scaffolds and chemical space An evolving expert system supported by machine learning Start from binding pattern from X-ray structure, use synthesizable building blocks and create a million molecules virtual library 2 nd step: Use AI to search chemical space Models molecular space as a vectorial space Searching is performed using integer programming methods (embarrassingly parallel) Searches for optimum molecules in the direction of increasing scores (typically binding affinity) Binding affinity Chemical space, multiple dimension Developed from Duke University technology (Inverse Design, exclusively licensed to Cloud)

11 11 Designing Targeted Chemical Space An evolving expert system supported by machine learning umans are creative, AI can be scaled Extract binding pattern from X-ray structure Extract roots while staying in 3D using the binding pocket characteristics and directionality Polar or Electronegative Functional Groups Ring X Mixed Functional Groups Linker atom and Ring or Fused Ring Search synthesizable universe of scaffold building blocks Explodes into functional groups Results in M molecules Z7 O Z6 Y5 V1 O X4 X3 W2

12 12 Artificial Intelligence Search Technology Proprietary proven AI search of vast molecular space for novel drug candidates 1 st step: Automatically designs targeted, novel scaffolds and chemical space An evolving expert system supported by machine learning Start from binding pattern from X-ray structure, use synthesizable building blocks and create a million molecules virtual library 2 nd step: Use AI to search chemical space Models molecular space as a vectorial space Searching is performed using integer programming methods (embarrassingly parallel) Searches for optimum molecules in the direction of increasing scores (typically binding affinity) Binding affinity Chemical space, multiple dimension Developed from Duke University technology (Inverse Design, exclusively licensed to Cloud)

13 13 Searching Chemical Space for the Best Drug Molecules Entire molecular space (10 60 molecules) An evolving expert system supported by machine learning Targeted chemical molecular space (100 million molecules) seed molecules molecules molecules Random sampling of molecules in targeted chemical space AI heuristic search of surrounding space to improve molecular properties Use multi-object optimization based on a disease profile to design best candidates The best molecular leads go for synthesis and experimental testing

14 14 A FEW EXAMPLES

15 15 Structure-Based Discovery of Dengue Virus Protease Inhibitors Ø Collaboration with Profs. Ashley Brown and George Drusano, M.D. Ø Orphan disease, however attracts a lot of DoD funding Ø Dengue fever is anthropod-borne virus transmitted by the A. aegypti mosquito 5 different serotypes; serotype cross-reactive immunity can cause severe reactions like emorrhagic fever and Dengue Shock Syndrome Ø Current treatment for Dengue Virus infection is symptomatic There are no vaccines or medications for Dengue fever Ø The target of this project is DEV2-S3 protease There are several X-ray/MR structures for the target owever, there are no small molecule inhibitors Very little binding data is available and those compounds are toxic and not very potent

16 16 Structure of DEV2 3S Protease 2M9P MR structure with peptide-like inhibitor Key residues that interact with inhibitor: SER135, IS51, GLU83-GLU86 (GLU-rich loop) 2 available inhibitors sets for model validation 6 Correlation: R1 Calc. LogIC50 (nm) O R2 3 n u Deng set Bodenreider set R Expt. LogIC50 (nm) R2 R1

17 17 Accurate Prediction of Binding Strength Expert system combines approaches tailored specifically for each project Multiple methods: QM/MM multi-scale/ multi-resolution Linear Interaction Energy (LIE) Free Energy Perturbation (FEP) Knowledge-Based scoring methods (e.g. Docking, SAR etc.) Maximizes the use of ligand data, experimental results, and regression studies

18 18 Validation of Computational Method Vs. Experimental Measurements: igh Accuracy QM/MM Calculation Multi-scale/Multi-resolution for small molecules in protein binding pockets Ligand described by QM, protein/solvent with MM, flexible protein and ligand, explicit water with full relaxation, sampling and solvation igh binding prediction accuracy (> 80%) LIE QM/MM calculated binding energy (kcal/mol) vs. experimental binding energy (kcal/mol) for BACE1 (red squares), SP90 (blue diamonds), PERK (orange triangles) and TYK2 (green triangles) *See Cloud Pharmaceuticals paper for more details Always validate your predictions with available data

19 19 Designing Drugs, not Molecules Building a disease profile to design best candidates Expert system to choose the best clinically relevant drug lead, including: Lowering side effects: probability of toxic side-effects, ADMET, Blood- Brain Barrier permeability (YES for CS, O for malaria) Increasing productivity: solubility, synthesizability Ensures protectable IP and freedom to operate: patent and literature searches Future plans: Machine Learning for PD/PK prediction Automated, state of the art, developed either in-house or externally

20 20 EXAMPLE 2: b Common Receptor (bcr) to Block EPO Side Effects Patients receiving elevated levels of EPO (erythropoietin) have increased risk of mortality: Excessive cardiovascular disease, higher mortality in sepsis patients & increased probability of cancer recurrence in breast cancer patients Inhibiting the EPO/bCR interaction can prevent these negative outcomes Cloud Pharmaceuticals designed several peptides to inhibit this previously un-druggable protein-protein interaction target Successful activity was measured in cell and animal models (zebra fish embryo) IP is being written Collaboration with Prof. Mark Segal, Chief of ephrology, University of Florida Medical School EPO bound to bcr

21 21 EXAMPLE 3: Designing Lipid Prodrug Molecules Exploiting the enzyme phospholipase A2 (PLA2) as a prodrug-activating enzyme for gut inflammation drug targeting Using free energy perturbations to accurately predict the activation of phospholipid-drug conjugates by PLA2 The calculated results correlated well with indomethacin in-vitro experimental data Status: ovel designs of another PL-drug set have been synthesized and show activity as predicted! Dahan et al., Curr. Top. Med. Chem., 2016(16)

22 Is AI the oly Grail of Drug Discovery?

23 23 What have we learned? Be cautious about pure machine learning solutions? To be sure, benefits are realizable But do not throw out successful methods entirely An age old AI saying that still holds today: AI is the raisins in the Raisin Brand (i.e., its not the entire solution)

24 24 Well, is AI the oly Grail of Drug Discovery? o! But it can increase accuracy and speed and decrease failures, and that should result in far less research expenditure and more patent life Best used in combination with other methods proven to work! Don t ignore molecular structure! You cannot ignore the search of novel molecular space!

25 25 What We ave Done Demonstrated Efficiency 25 disease targets, 18 months After synthesis, 100% of targets have bio-active molecule Time to design new molecules for a target is 3 months ~100x more efficient than industry average Proven Effectiveness Use AI for multi-object optimizations and searches Penetrates nucleus of a cell (eif4e) Penetrates blood-brain barrier (CS application) Does not penetrate blood-brain barrier (cancer application, Malaria) Selectivity drives down toxicology risk Results in addressing previously undruggable targets (βcr) Significant correlation between calculated and measured binding affinity Selective inhibition of JAK3 in protein assay for Rheumatoid Arthritis Block EPO side effects by inhibiting bcr - promising results in Zebra Fish embryo model Combat sepsis in mice models stop Serpins aggregation

26 26 Contact Ed Addison, CEO