Advanced in silico Drug Design KFC/ADD Drug design intro. Karel Berka, Ph.D. Jindřich Fanfrlík, Ph.D. Martin Lepšík, Ph.D.

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1 Advanced in silico Drug Design KFC/ADD Drug design intro Karel Berka, Ph.D. Jindřich Fanfrlík, Ph.D. Martin Lepšík, Ph.D. UP Olomouc,

2 Motto A pharmaceutical company utilizing computational drug design is like an organic chemist utilizing an NMR. It won t solve all of your problems, but you are much better off with it than without it. DAVID C. YOUNG Disclaimer: Authors of this lecture series did not designed any drug in common use. Yet.

3 Course program Monday 13:00 Drug design intro (Berka) 14:00 Databases of small molecules (ZINC), drug representations (Berka) 15:00 Properties of biologically active compounds (Fanfrlik) 16:00 Biomolecular targets (Lepšík) 17:00 Training target database search (Berka) Tuesday 9:00 Noncovalent interactions in drugtarget binding (Fanfrlík) 10:00 Molecular docking intro (Berka) 11:00 Training visualization and simple docking (Berka) Tuesday 14:00 Advanced docking flexibility, consensus, ensemble docking (Fanfrlík) 15:00 Advanced scoring (WaterMap, MM-PBSA, FEP, QM) (Lepšík) 16:00 Training scoring (Lepšík) Wednesday 9:00 QSAR, ADMET (Berka) 10:00 Advanced virtual screening (Fanfrlík) 11:00 Future of drug design (Lepšík

4 KFC-ADD Exam Requirements Project Drug design for selected problem (short and clear drug design report, 1-4 pages, short intro with defined task, short methodology, results with visuals, conclusion) Oral exam Discussion over project and strengths and weaknesses of selected approach

5 Literature Young, D.C. Computational Drug Design. Wiley, Young D.C. Computational Chemistry, a Practical Guide for Applying Techniques to Real World Problems. Wiley, Leach AR. Molecular Modelling - Principles and Applications (2 edition). Pearson Education, Alvarez J. & Shoichet B. (Eds.). Virtual Screening in Drug Discovery. Taylor&Francis, Berka K. & Bazgier V. Racionální návrh léčiv pomocí in silico metod. UP Olomouc, 2015 (in Czech)

6 History of Drug Design 1806 A. Cherkasov

7 History of Drug Design Time New Sources Testing Subjects - ancient & plants, poisons (Paracelsus) humans middle ages minerals... natural sources morphine (first extracted) humans chemicals (chinin) humans (prisoners) synthetics, pigments animals animals, isolated organs enzymes, cell lines (HeLa) High throughput libraries recombinant proteins chemical libraries chips, virtual screening, ADMET testing

8 Sources of Drugs All drugs by source, registered 01/ /2006, FDA, n = 1184 S*; 4% V; 4% N; 5% S*/NM; 10% S/NM; 10% S; 30% B; 14% B biologicals, N nature compounds, ND nature compounds derivatized, S synthetic compounds, D. J. Newman and G. M. Cragg, J. Nat. Prod. 70, (2007) ND; 23% S/NM synthetics mimicking natural compounds, S* - synthetic, with pharmacophore from natural compounds V - vaccines

9 Drug Design Project Illness Identification of lead compound (2-5 let) Isolation of cause (many years) Preclinical testing on animals (1-3 years) Formulation (1-3 years) Human clinical trials (2-10 years) FDA approval (2-3 years) Heal and Sell

10 Vocabulary Target Biomolecule interacting with the drug Lead Base molecular structural motif of developed drug Hit Compound with positive hit in initial screening Candidate compounds Selected compounds used for next testing Efficacy Qualitative property (drug heals or not) Activity Quantitative property dosage needed for effect to happen (pm great, nm excellent, μm sufficient, mm well ) Bioavailability Availability of compound in site of target in necessary concentration

11 Eroom s Law Decline in pharmaceutical R&D efficiency halved per 9 years 'better than the Beatles' problem 'cautious regulator' problem 'throw money at it' tendency 'basic research brute force' bias. Scannell JW, Blanckley A, Boldon H, Warrington B: Nature Reviews Drug Discovery 11, (2012) doi: /nrd3681

12 Why in silico Drug Design? Identification of new drug: hard problem Identification of target-drug pair is not simple ADMET Side-effects Expensive problem Expenditures per 1 drug development USD 1 + expenses for production, patents, distribution New drugs are expensive >1 000 USD/dose of drug Tufts Center for the Study of Drug Development, SÚKL, 3Q 2011, average price tag for most expensive drug category in CZ (over 10kKč)

13 Hard Problem Human genom ~ ORF Alternative splicing => ~ proteins ~ structures known in PDB (8 000 unique) 2 10 years from lead molecule identification to clinical testing (patents last 20 years) 1 successful out of 10 drug development projects

14 Possible Obstacles Nonexistent testing model Example: HIV is human disease! Ethically not possible to test directly on people (cf. OS) Rare disease orphan disease Future sales would not pay for regular development Orphan drug have lower requirements for registration and individual incentives Too low activity of found drug Too toxic, bad bioavailability Active compounds are already patented Me2drugs Product has to be just as good as the one from competition and patentable under our name

15 Illness Type Enzyme overproduction - some cancer types Inhibition (e.g. kinase inhibitors) High response of receptor COX in pain Antagonists (e.g. pain relievers) Low response of receptor neurological GPCRs Agonists (e.g. serotonin receptor agonists) Regulation peptide CGRP peptide in migraine Antibodies (e.g. biologicals) RNA RNAi, RNA aptamers Emerging field Small ligand with protein

16 Most Typical Mechanism Lock and Key Analogon, 1894

17 Expensive Problem Experiment Virtual screening Biochemical analysis Cell culture testing Acute toxicity on mice Protein structure evaluation with X-Ray Efficiency testing on animals Chronic toxicity on rats Clinical testing on volunteers Cost per 1 compound 100 Kč Kč Kč Kč Kč Kč Kč Kč Lower price tag allow testing of more drug candidates David C. Young - Computational Drug Design: A guide for computational and medicinal chemists. Wiley-Blackwell, New York, 2009, ISBN

18 Unknown target structure Known target structure Possibilities of in silico Drug Design Known ligand Structure-based drug design (SBDD) Docking Unknown ligand De novo design Ligand-based drug design (LBDD) 1 or more ligands Similarity search Several ligands Pharmacophore Large number of ligands (20+) Quantitative Structure-Activity Relationships (QSAR) CADD not possible some experimental data needed ADMET filtering

19 SIMILARITY SEARCHES 19

20 Similarity Search Search for similar structures to lead 2D Sub-Structures 3D Sub-Structures 3D Conformational flexibility 20

21 Similarity to Natural Ligand NH 2 N(CH 3 ) 2 HO H 3 C H N S N O O N H H 5-Hydroxytryptamine (5-HT) Serotonin (a natural neurotransmitter synthesized in certain neurons in the CNS) Sumatriptan (Imitrex) Used to treat migrain headaches known to be a 5-HT 1 agonist 21

22 2D Sub-Structure Functional groups Connectivity Example. Halogen on aromatic ring together with carboxylic group [F,Cl,Br,I] O O O O Cl F O Cl O O N N O O N F F N 22

23 Steric Energy (kcal/mol) 3D Sub-Structure Distances in 3D play more important role Bioisostericity similar groups in 3D Usually storage only lowest energy structure 6 O A O (s1) C(u) O(s1) Å Å Å A [O,S] Dihedral angle 23

24 Bioisostericity Young, D.C. Computational Drug Design. Wiley,

25 How to calculate molecular similarity Quantitative assessment of similarity/dissimilarity of structures need a numerically tractable form molecular descriptors, fingerprints, structural keys Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics. E= Euclidean distance T = Tanimoto index (distance in XYZ) (similarity of bit vectors) E( x, y) n x i y i i 1 2 T( x, y) B( x) B( x & y) B( y) B( x & y)

26 Paradox of Similarity It is not that simple Aminogenistein (x cystic fibrosis) Pargyline (x hypertensis) 7-Hydroxy-2-(4-nitro-phenyl)-chromen-4-one N-benzyl-N,1-dimethyl-2-propynylamine 26

27 PHARMACOPHORE

28 Pharmacophore structural motive (geometrical restrictions on functional groups) important for biological - mostly pharmacological activity Analogous to chromophore Bojarski, Curr. Top. Med. Chem. 2006, 6, 2005.

29 Prerequisition: Search for pharmacophore All active compounds in training set bind to common active site Pharmacophore query preparation Identification of characteristic functional groups (hydrogen bonds acceptors and donors, lipophilic groups, charge distribution) Pharmacophore search Search for pharmacophore query in all molecules in DB Scaffold hopping possible No need for receptor structure (can be useful for query generation)

30 VIRTUAL SCREENING

31 Virtual screening Multiple ligands => Pipelining (Pipeline Pilot, KNIME, etc.) Pipeline Pilot (Accelrys)