- 2013/ D Structures of Biological Macromolecules Part 5: Drug Research and Design Jürgen Sühnel Supplementary Material: Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena Centre for Bioinformatics Jena / Germany

Example of Drug Discovery

Phases of Clinical Trials Phase I: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects. Phase II: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety. Phase III: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely. Phase IV: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

Example of Drug Discovery

Pacific yew tree (Eibe) Example of Drug Discovery

Drug Research is the Search for a Needle in a Haystack.

Development of Drug Research

Drug Timeline

Drug Timeline

Cost for discovering and developing a new drug: several € 100 million up to € 1000 million (average € 802 M ) Time to market: 10 – 15 years Costs in Drug Research

Global Company Sales 2006

Top Ethical Drugs by Sales in (Lowering blood cholesterol) (Asthma treatment) (Inhibits blood clots) (Proton pump inhibitor; treatment of dyspepsia, peptic ulcer disease, …) (Calcium channel blocker; anti-hypertensive agent)

New Products Marketed for the First Time

Molecular Conceptor Disciplines Involved in Drug Development

Molecular Conceptor The Role of Molecular Structure

Molecular Conceptor The Pharmacophore Concept

Mechanisms of Drug Action – Definitions I

Mechanisms of Drug Action – Definitions II

Molecular Conceptor Serendipity - Penicillin

Serendipity - Aspirin Molecular Conceptor Serendipity - Aspirin

Strategies in Drug Design

Target identification Lead discovery Lead optimization Ligand-based design Receptor-based design (Docking) Database screening (Virtual screening) Supporting combinatorial chemistry Computational Approaches to Drug Research

3D Structures in Drug Design

Lead Structure Identification

Lead Structure Search Pipeline

Lead Structures: Endogenous Neurotransmitters

Lead Structures: Endogenous Neurotransmitters Neurotransmitters are chemicals that are used to relay, amplify and modulate electrical signals between a neuron and another cell. Acetylcholine: Acetylcholine:voluntary movement of the muscles Noradrenaline: Noradrenaline:wakefulness or arousal Dopamine: Dopamine:voluntary movement and emotional arousal Serotonin: Serotonin:sleep and temperature regulation GABA: GABA:(gamma aminobutryic acid) - motor behaviour

Lead Optimization

Ligand-based Design: What is QSAR ?

Ligand-based Design:Basic Requirements for QSAR Studies Ligand-based Design: Basic Requirements for QSAR Studies

Ligand-based Design: QSAR Hansch analysis is the investigation of the quantitative relationship between the biological activity of a series of compounds and their physicochemical substituent or global parameters representing hydrophobic, electronic, steric and other effects using multiple regression correlation methodology.

Ligand-based Design: QSAR

Ligand-based Design: QSAR Parameters

Ligand-based Design: QSAR Parameters - Lipophilicity

Ligand-based Design: QSAR Parameters

Ligand-based Design: QSAR Parameters

Ligand-based Design: QSAR Parameters  - reaction constant  - substituent constant

Ligand-based Design: QSAR Parameters

Ligand-based Design: QSAR Parameters

Ligand-based Design: QSAR Parameters

Ligand-based Design: A QSAR Success Story

pI 50 – concentration of test compound required to reduce the protein content of cell by 50% Ligand-based Design: A QSAR Success Story

Ligand-based Design: 3D-QSAR CoMFA

Molecular Superposition of Vitamin D Receptor Ligands

Ligand-based Design: 3D-QSAR CoMFA

Ligand-based Design: 3D-QSAR CoMFA

Partial least squares regression (PLS regression) is a statistical method that finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models.statisticallinear regressionpredicted variablesobservable variables PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. PLS-regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases.matriceslatent variablecovariancemulticollinearity PLS regression is an important step in PLS path analysis, a multivariate data analysis technique that employs latent variables. This technique is often referred to as a form of variance-based or component-based structural equation modeling.path analysislatent variablesstructural equation modeling Partial least squares was introduced by the Swedish statistician Herman Wold, who then developed it with his son, Svante Wold, a professor of chemometrics at Umeå University. An alternative term for PLS (and more correct according to Svante Wold [3] ) is projection to latent structures, but the term partial least squares is still dominant in many areas. It is widely applied in the field of chemometrics, in sensory evaluation, and more recently, in the analysis of functional brain imaging data. [4]Herman WoldchemometricsUmeå University [3]chemometrics [4]

Electrostatic and Van-der-Waals Interactions

Comparative Molecular Field Analysis Ligand-based Design: 3D-QSAR CoMFA

Molecular Conceptor Receptor-based Design (Structure-based Design)

Molecular Conceptor Receptor-based Design (Structure-based Design)

Molecular Conceptor Receptor-based Design: Docking

Molecular Conceptor Receptor-based Design: Docking

Molecular Conceptor Receptor-based Design: Docking

Molecular Conceptor Hydrophobic Amino Acids

Molecular Conceptor Receptor-based Design: Docking

Molecular Conceptor H-Bond Properties of Amino Acids

Molecular Conceptor Receptor-based Design: H-bond Effect IC50 - Drug concentration required for 50% inhibition of a biological effect

Receptor-based Design: H-bond Effect

Molecular Conceptor Charge Properties of Amino Acids

Molecular Conceptor 116. Receptor-based Design: Salt Bridge

Molecular Conceptor Receptor-based Design: Docking

Molecular Conceptor Receptor-based Design: SAR (Pharmacophore Features)

Molecular Conceptor Receptor-based Design: DNA Receptor

Molecular Conceptor Receptor-based Design: DNA Intercalating Agents

Molecular Conceptor Receptor-based Design: DNA Intercalating Agents

Receptor-based Design: AIDS Drugs

Combinatorial Diversity in Nature

ww.kubinyi.de Classical vs. Combinatorial Chemistry

ww.kubinyi.de Combinatorial Library

ww.kubinyi.de Combinatorial Library

ww.kubinyi.de Types and Features of Combinatorial Libraries

Virtual Screening: Select subsets of compounds for assay that are more likely to contain active hits than a sample chosen at random Time Scales: Docking of 1 compound 30 s (SGI R10000 processor) Docking of the 1.1 million data set6 days (64-processor SGI ORIGIN) ACD-SC: Database from Molecular Design Ltd. Agonists: Known active compounds Docking of ligands to the estrogen receptor (nuclear hormone receptor) Receptor-based Design: Virtual Screening

Compounds are likely to have a good absorption and permeation in biological systems and are thus more likely to be successful drug candidates if they meet the following criteria: 5 or fewer H-bond donors 10 or fewer H-bond acceptors Molecular weight less than or equal to 500 daltons Calculated log P less than or equal to 5 „Compound classes that are substrates for biological transporters are exceptions to the rule“. Lipinski‘s „Rule of Five“ Druggable compounds

ADME ADME

The Future: Pharmacogenomics and Personalized Medicine

Prediction Issues