Computer Added drug designing by Center for advance technology

Tools and methods

1. Introduction to Drug Discovery and Drug-Like Property Analysis (The Bioinformatics Approach) Dr. Mohammad Hayatul Islam Director CATR

2. Introduction The field of science in which biology, computer science, and information technology merge into a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned. There are three important sub- disciplines within bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data and; the development and implementation of tools that enable efficient access

3. Basis of Drug Designing Drug discovery is the process through which potential new therapeutic entities are identified, using a combination of computational, experimental, translational, and clinical models. The drug is commonly known as an organic or small molecule that activates or inhibits biomolecule function. D rug discovery requires a lengthy, costly, difficult, and inefficient process with a high attrition rate of new therapeutic discovery. 

4. What is Drug

5. How drugs work?

6. Sources of Drugs

7. Basis of Drug Designing

8. Basis of Drug Designing

9. Complexity of Drug Discovery

10. What is CADD A combination of advanced computational techniques, biological science, and chemical synthesis was introduced to facilitate the discovery process, and this combinational approach enhanced the scale of discovery which is referred to as computer-aided drug design.  Computer-Aided Drug Design (CADD) emerged as an efficient means of identifying potential lead compounds and for aiding the developments of possible drugs for a wide range of diseases.

11. Why CADD?

12. Methodologies and strategies of CADD

13. Approaches Used ? Computer-Aided Drug Designing Structure-based drug designing Direct Designing utilizes protein three-dimensional (3D) structural information Ligand-based drug designing Indirect designing independent of any information about the molecular target.

14. Structure-Based Drug Designing Draggability Prediction Identification and Determine of Protein Structure Identify Interaction Sites Molecular modelling & Docking Ligand-Based Drug Designing Ligand identification Library preparation Pharmacophore modelling QSAR prediction Virtual Screening , Molecular Dynamics Lead Candidate Steps involved

15. Structure based drug design What we need?

16. Structure based drug design What we need?

17. Why Modelling ? Experimental determination of structure is still a time consuming and expensive process. Number of known sequences are more than number of known structures. Structure information is essential in understanding function.

18. Drug Targets Molecule or Structure within the organism linked to a particular disease, whose activity can be modified by drug Distribution of targets by biochemical criteria Distribution of targets bin therapeutic areas

19. Technology is impacting CADD process

20. ADMET/Lipinski’s Profiling Protein-Ligand Docking PHARMACOKINETIC ANALYSIS MOLECULAR DOCKING Stability of Protein- Ligand complex MOLECULAR DYNAMICS SIMULATION Technology is impacting CADD process

21. Pharmacokinetics “What the body does to the drug”

22. Pharmacokinetics The study of the disposition of a drug The disposition of a drug includes the processes of ADME Absorption Distribution Metabolism Excetion Toxicity

23. Why Drugs Fail?

24. What makes a good drug?

25. Absorption Gastrointestinal absorption of drug substances involves a complex mechanism. Due to several factors that are classified mainly into the physiological effects, the physicochemical effects, and the formulation effects. The absorption of orally administered drugs is basically characterized by one of three mechanisms that include the facilitated diffusion, the passive diffusion, and the active transport, depending on factors such as the particle size and the diffusion coefficient of the drug as well. In silico models have been applied to evaluate the influence of gastric pH on the exposure of drugs of weak bases, and in other cases, these models were used to predict the bioavailability, gastric acid function, and the risks associated. Absorption can be determined by various techniques, and in some cases, it can be described in the terms of either the permeability or the solubility of the drug.

26. Factors responsible for Absorption The Rule of Five - formulation

27. Factors responsible for Absorption Absorption & Ionization Non-ionised drug More lipid soluble drug Diffuse across cell membranes more easily First Pass Metabolism

28. Distribution The pharmacokinetic profile of a drug substance is determined by various parameters including tissue distribution. The prediction of drug distribution throughout the body is basically divided into three main areas of examination, which are the BBB permeability, the volume of distribution (VD), and the plasma protein binding (PPB). Nowadays, several methods are being used to predict drugs tissue distribution, and the prediction can be achieved by examining either the volume of distribution of drugs at the steady state or the tissue:plasma ratios. In Silico Prediction of PPB The PPB of drugs can affect both the pharmacokinetics and pharmacodynamics of drugs since generally, it is accepted that only the unbound (free) fraction of the drug is active, it is important to estimate the PPB of drug candidates. The most important protein involved in the binding with drugs in plasma is the human serum albumin, which can bind to a wide variety of endogenous and exogenous molecules.

29. Distribution Many of these models are based on the available 3D crystal structures of albumin which can be utilized in performing docking studies to predict the binding of molecules with albumin. Other major proteins that also have the ability to bind with drugs in plasma are the alpha1-acid glycoprotein and lipoproteins, which have received less attention with regard to prediction models developed in comparison with the human serum albumin. Determined by: Partitioning across various membranes Binding to tissue components Binding to blood components (RBC, plasma protein) Physiological volumes

30. Distribution All of the fluid in the body (referred to as the total body water), in which a drug can be dissolved, can be roughly divided into three compartments: intravascular (blood plasma found within blood vessels) interstitial/tissue (fluid surrounding cells) intracellular (fluid within cells, i.e. cytosol ) The distribution of a drug into these compartments is dictated by it's physical and chemical properties Apparent volume of distribution ( Vd ) = Amt of drug in body/plasma drug conc.

31. Distribution Blood flow: rate varies widely as function of tissue Muscle = slow Organs = fast Capillary structure: •Most capillaries are “leaky” and do not impede diffusion of drugs •Blood-brain barrier formed by high level of tight junctions between cells •BBB is impermeable to most water-soluble drugs

32. Distribution

33. Distribution Blood Brain Barrier Disruption by osmotic means Use of endogenous transport systems Blocking of active efflux transporters Intracerebral implantation Etc

34. Distribution Plasma Protein Binding Many drugs bind to plasma proteins in the blood steam Plasma protein binding limits distribution. A drug that binds plasma protein diffuses less efficiently, than a drug that doesn’t.

35. Metabolism Drug metabolism has been recently estimated as one of the major parameters that has shown to be taken into serious consideration during the discovery, development, and design of drug candidates. Some research reports stated that drugs metabolism is the most difficult parameter to predict as compared to other pharmacokinetic parameters because the process of metabolism is a very complex process that involves various enzymatic activities that vary among individuals due to different genetic factors. Different computational (in silico) models were successfully applied to estimate relative predictions regarding the metabolism of some drugs. Some aspects should be optimized during the assessment of a drug’s metabolism profile at the early stages, and these aspects include the metabolic routes, stability, and interactions along with the kinetics of metabolizing enzymes as well.

36. Metabolism The cytochrome P450 (CYP) is considered to be the most influential enzyme in the drug metabolism, which led to the development of many models such as QSAR for the prediction of the metabolism of molecules by the CYP enzyme.

37. Metabolism Phases of Drug Metabolism Phase I Reactions Convert parent compound into a more polar (=hydrophilic) metabolite by adding or unmasking functional groups (-OH, -SH, -NH2, -COOH, etc.) eg . Oxidation Often these metabolites are inactive May be sufficiently polar to be excreted readily Phase II Reactions Conjugation with endogenous substrate to further increase aqueous solubility Conjugation with glucoronide , sulfate, acetate, amino acid

38. Metabolism Mostly occurs in the liver because all of the blood in the body passes through the liver

39. Metabolism The Most Important Enzymes Microsomal cytochrome P450 monooxygenase family of enzymes, which oxidize drugs Act on structurally unrelated drugs Metabolize the widest range of drugs. Found in liver, small intestine, lungs, kidneys, placenta • Consists of > 50 isoforms • Major source of catalytic activity for drug oxidation • It’s been estimated that 90% or more of human drug oxidation can be attributed to 6 main enzymes: • CYP1A2 • CYP2D6 • CYP2C9 • CYP2E1 • CYP2C19 • CYP3A4 In different people and different populations, activity of CYP oxidases differs.

40. Excretion In Silico Prediction of Drug Excretion Excretion refers to the process by which the body gets rid of the waste/toxic products. The drug excretion process can be achieved by either the kidney and/or the liver where drugs are eliminated in the form of urine or bile, respectively. The most important factor that determines the proper drug removal mechanism is the molecular weight, where substances of relatively small molecular weights are mainly removed through urine. Passive excretion can be predicted based on some approaches that include the flow rate, lipophilicity, protein binding, and the pKa value. 4 After the prediction of a drug’s excretion profile, collected information have to be integrated into a predictive model that provides a complete model describing the behaviour of the substance during the different stages of drug discovery and development.

41. Toxicity In Silico Prediction of Toxicity Profile Conventionally, toxicity was tested by using laboratory animals. In recent improvements, new approaches have been conducted for toxicity optimization, which have been reported to minimize the risks of animal toxicity testing by the replacement with much safer alternatives. In silico toxicology generally refers to predictive science and toxicology computational techniques provide toxicity databases that make it possible to perform QSAR modeling . There are various reasons that stand behind the importance of in silico prediction of drugs toxicity, such as the increasing demand to reduce animal testing, as well as the more suitable toxicity prediction that can be obtained by the use of computational approaches. In silico prediction methods that are specialized for the prediction of drugs’ toxicity can be classified into methods that predict the systemic toxicity and the other methods specifically predict the toxicity for a certain organ. 2

42. Lipinski's rule of five / Rule of 5 Lipinski's rule of five is a rule of thumb that describes the drugability of a determinate molecule. This rule helps to determine if a biologically active chemical is likely to have the chemical and physical properties to be orally bioavailable . The Lipinski rule bases pharmacokinetic drug properties such as absorption, distribution, metabolism and excretion on specific molecular properties such as: No more than 5 hydrogen bond donors No more than 10 hydrogen bond acceptors  Molecular mass less than 500 Da Partition coefficient not greater than 5 The violation of 2 or more of these conditions predicts a molecule as a non-orally available drug. Drugability

43. Databases of Molecules Pubchem PubChem is the world's largest collection of freely accessible chemical information. Search chemicals by name, molecular formula, structure, and other identifiers. Find chemical and physical properties, biological activities, safety and toxicity information, patents, literature citations and more. PubChem (nih.gov) NPASS (Natural Product Activity & Species Source Database) Integrating Species Source of Natural Products & Connecting Natural Products to Biological Targets via Experimental-derived Quantitative Activity Data Version 2.0 NPASS Database ( bidd.group ) 32,287 Source Organisms 96,481 Natural Products 7,753 Biological Targets 958,866 Activities Records Zinc Database (Natural compounds catalog) Natural Products occupy an important part of small molecule space because they are recognized by at least two proteins: the end of their biosynthetic pathway and their evolutionary biological target. Most of these compounds are for sale, but some are collabocules . Over a third of all drugs are natural products or similar to one. Natural products may be expensive. Please also see the  Special Subsets  many of which feature natural products and metabolites. Natural Products Catalogs | ZINC Is Not Commercial - A database of commercially-available compounds (docking.org) ZINC (docking.org)

44. Databases of Molecules NPACT ( Naturally occurring Plant based Anticancerous Compound-Activity-Target Database) NPACT  is a curate database of Plant derived natural compounds that exhibit anti-cancerous activity. It contains 1574 entries and each record provides information on their structure, properties (physical, elemental and topological), cancer type, cell lines, inhibitory values (IC 50 , ED 50 , EC 50 , GI 50 ), molecular targets, commercial suppliers and drug likeness of compounds. NPACT concentrates on anti-cancer natural compounds found in plants only. NPACT is unique in providing bioactivities of these natural compounds against different cancer cell lines and their molecular target. crdd.osdd.net/ raghava / npact / ChEMBL ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. ChEMBL Database (ebi.ac.uk) Drug Bank Drug bank is a vital resource for your pharmaceutical research, offering comprehensive & reliable drug data, structured for immediate use or easy software integration.

45. In Silico ADME/ Tox Analysis ProTox -II server ProTox -II incorporates molecular similarity, fragment propensities, most frequent features and (fragment similarity based CLUSTER cross-validation) machine-learning, based a total of 33 models for the prediction of various toxicity endpoints such as acute toxicity, hepatotoxicity , cytotoxicity , carcinogenicity, mutagenicity , immunotoxicity , adverse outcomes (Tox21) pathways and toxicity targets. https://tox-new.charite.de/protox_II/ Swissadme server used to analyzed Drug likeness, ADME and toxicity of compounds http://www.swissadme.ch/ ADMET Lab 3.0 used to analyzed Drugs/compounds Pharmacokinetic study. https://admetmesh.scbdd.com/service/screening/index PreADMET server used to analyzed Drug likeness, ADME and toxicity of compounds. https://preadmet.qsarhub.com/

46. Druggability Parameters Rule MW HBD HBA XLogP Other Factors Lipinski’s Rule <500 <5 <10 <5 GSK Rule ( GlaxoSmithKline ) <400 <4 Pfizer Rule “ “ “ <3 <75 tPSA Ghose filter 160-480 -0.4- 5.6 Total No. of Atoms 20-70 molar refractivity 40-130 Golden Triangle The Golden Triangle is a visualization tool to help the simultaneous optimization of absorption and clearance of drugs. When plotting molecular weight versus distribution coefficient at pH 7.4 (log  D  7.4) for a series of molecules. Veber Rule Rotatable bond <12 tPSA <140 Egan Rule Considers good bioavailability for compounds with tPSA ≤ 132 Å2, logP ≤ 6 Muegge Rule tPSA ≤ 150 Å2, logP ≤ 5, Rotatable bond <15 rest like Lipinski

47. Molecular Docking Docking is the identification of the low-energy binding modes of a small molecule or ligand within the active site of a macromolecule, or receptor, whose structure is known. Calculate the differential binding of a ligand to two different macromolecular receptors Predicts the preferred orientation of one molecule to second molecule to form stable complex Target protein Ligand Complex

48. Categorize of Docking Protein-Protein Docking Protein-Ligand Docking

49. Tools Used? PyRx HADDOCK GOLD

50. THANKYOU