Abt. Simulation biologischer Systeme WSI/ZBIT, Eberhard-Karls-Universität Tübingen
Drug Design 2
Oliver Kohlbacher Winter 2009/2010
1. Introduction and Overview
What is Drug Design? Design
Deliberate creative act Drug
Substance causing a biochemical effect in the human body
Key questions: • What to put into a pill? • How to answer the first question using a computer?
?
Drug Design
Structural Biology
Bioinformatics Chem-
informatics
Theoretical Chemistry
Medicinal Chemistry
Pharmacology Pharmacy
Medicine
Economics DRUG DESIGN
Basic Definitions
Drug, noun
a substance that has a physiological effect when ingested or otherwise introduced into the body, in particular
• a medicine, esp. a pharmaceutical preparation • a substance taken for its narcotic or stimulant
effects, often illegally
Apple Dictionary, rel. 2.0.25
Basic Definitions
Medicine a compound or preparation used for the treatment or prevention of disease, esp. a drug or drugs taken by mouth
Apple Dictionary, rel. 2.0.25
What is Drug Design? Design
Deliberate creative act Drug Design
Design of a drug for a specific (medical) application
Key question What to put into a pill?
?
Trials Biol. Data
Drug Discovery Pipeline
Drug Design accelerated by
bio- und cheminformatics
Target ID Lead ID Optimization Approval
Contents Covered in DD1 1. Basics
– Poisons and Drugs – Historical Overview
– Pharmaceutical basics
– Molecular basics of drug action
2. Target Identification – Target discovery
• Paths & networks,
• Transcriptomics, proteomics, metabolomics
– How many targets are there?
– Target validation 3. Lead Identification
– Origin of lead structures – High-throughput Screening (HTS) – Combinatorial Chemistry – Library design Topics highlighted in red are
relevant for this course too!
Contents Covered in DD1 4. 3D Structure
• Molecular mechanics • Molecular Interactions • Force fields
• Energy minimization • 3D structure generation • Conformational analysis • Comparison of 3D structures
• Molecular recognition
5. Molecular interactions – Receptor structures – Receptor-ligand interactions
Contents Covered in DD1 6. Docking
– Problem definition – Rigid docking
• Algorithms – Flexible docking
• FlexX • Evolutionary algorithms
– Scoring functions • Force field-based • Empirical • Knowledge-based • Comparison of current scoring functions
– Virtual Screening 7. de novo design
– Problem definition – Algorithms
Drug Design 2 • DD1 and DD2 can be
taken independently • Together DD1 and 2
give a comprehensive overview of computer-aided drug design
• DD1 provides some of the pharmaceutical basis for DD2, we will not repeat this here
• DD2 has a focus on ligand-based drug design and algorithmic issues there
Bioinformatics 1
Bioinformatics 2
Drug Design 1 Microarray Bioinformatics
Computational Proteomics
Protein Struct. & Modeling
Protein Evolution
Drug Design 2
Contents of Drug Design 2 1. Introduction
– Basics of drug design (recap of DD1)
2. Chemoinformatics – Representation of molecules – Molecular graphs
• Graph isomorphism • Graph canonization
– Software tools
3. Topological similarity – Comparison of structures
• Graph-based comparisons • Fingerprint-based comparisons
– Substructure search – Structural diversity – Clustering of molecular databases
Contents of Drug Design 2 4. Quantitative Structure-Activity Relationships (QSAR)
– Linear models • Methods • Applications
– Nonlinear QSAR methods – 3D-QSAR – COMFA/QSiAR – Proteochemometrics
5. Prediction of bioavailability and toxicity (ADMET) • ADME • Toxicity predictions
Overview of Class Dates Date Topic
20.10.2009 Introduction (today)
27.10.2009 Chemoinformatics Basics
03.11.2009 2D Similarity I
17.11.2009 2D Similarity II
24.11.2009 3D Structure of Ligands
01.12.2009 3D Similarity I
08.12.2009 3D Similarity II
15.12.2009 QSAR I
12.01.2010 QSAR II
19.01.2010 QSAR III
26.01.2010 QSAR IV
03.02.2010 ADMET
10.02.2010 Presentations
10.02.2010 Summary
22.-26.02.2010 Finals (oral exam)
Course Requirements To pass this course you must: • regularly and actively participate in the weekly
problem sessions, • regularly hand in assignment sheets, • work on a small project and present it orally in front
of class, and • participate in the final exam
• You have to work on assignments alone (no groups!)
Course Credits & Grading • Credits
– MSc, 4 LP, Module “Wahlpflicht Bioinfo” – Diplom: 3 SWS (prakt. Informatik)
• Grade – 25% assignments – 25% project – 50% finals
• Finals: oral exam (30 minutes) covering the contents of the whole lecture
• Finals will be held during the last week of the semester
Materials
• On our website you can find http://www-bs.informatik.uni-tuebingen.de/Teaching/WS2009/DD2/
– relevant publications, – these slides (PDF), and – links to
• useful software • web-based servers • webpages with additional information
• Text books
Text Books
Johann Gasteiger, Thomas Engel (Hrsg.): Chemoinformatics. A Textbook, Wiley-VCH, 2003
Overview of some key methods in chemoinformatics. (~75€)
Text Books
Andrew Leach, Valerie Gillet: An Introduction to Chemoinformatics. Kluwer Academic Publishers, 2003
Very concise overview of some of the key elements of chemoinformatics. (~75€)
Text Books
Wirkstoffdesign: Entwurf und Wirkung von Arzneistoffen Spektrum-Verlag, 2009 (available in German only)
Good overview of drug design from a (mostly industrial) perspective. Numerous examples of drugs and mechanisms of actions. Good overview of relevant experimental and theoretical methods. Relevant for this lecture are mostly pp. 247-300. (~90 €)
Text Books
• Special section of the library dedicated to this lecture – Holds the aforementioned text books
– Holds additional interesting text books on • Medicinal chemistry
• Theoretical chemistry
• Pharmacology
• Toxicology
• Additional information on the website
History of Drug Design - Overview
History of drug development consists of several phases
– Traditional medicine: medicines derived from nature, mostly from plants, discovery by trial and error
– Animal experiments and organic chemistry enable the systematic synthesis of new drugs
– Molecular in vitro test systems allow activity assays without animal experiments
– Structural biology and theoretical chemistry enable accelerated, rational design of drugs
Claudius Galenus • Most accomplished physician of
the Roman age (129-200 AD) • Physician to gladiators, later on
personal physician to the emperor
• Fundamental insights into human anatomy
• Humorism – Disease as an imbalance of the four
humours (blood, phlegm, yellow bile, black bile)
– Theoretical foundation of medicine of the next few centuries
Claudius Galenus
http://upload.wikimedia.org/wikipedia/commons/d/da/Galen_(Zeichnung).jpg
Paracelsus • Philippus Theophrastus Bombastus von
Hohenheim, aka Paracelsus (1493-1541 AD)
• Author of numerous texts on medicine, theology, astrology (after 1520 AD)
• Refuting some of the ideas of Galenus • Often in conflict with his contemporaries:
„Paracelsus gained a reputation for being arrogant, and soon garnered the anger of other physicians in Europe. He held the chair of medicine at the University of Basel for less than a year; while there his colleagues became angered by allegations that he had publicly burned traditional medical books. He was forced from the city after having legal trouble over a physician's fee he sued to collect.“
Paracelsus project of Zürich university: www.paracelsus.unizh.ch http://en.wikipedia.org/wiki/Paracelsus
Paracelsus
• Life as chemical process • Ingredients of plants
(“quinta essentia“ – quintessence) are responsible for their effects
• only dose makes the poison (dosis facit venenum)
• Pioneer of pharmaceutical chemistry
• Still mostly influenced by alchemy
• “Doctrine of signatures“
History of Drug Design - Overview
History of drug development consists of several phases
– Traditional medicine: medicines derived from nature, mostly from plants, discovery by trial and error
– Animal experiments and organic chemistry enable the systematic synthesis of new drugs
– Molecular in vitro test systems allow activity assays without animal experiments
– Structural biology and theoretical chemistry enable accelerated, rational design of drugs
Organic Chemistry • Drugs derived from plants and animals were
predominant until the beginning of the 19th century
• Development of organic chemistry – Basic understanding of chemical structure
1858 Kekulé suggests to represent chemical bonds by lines
1861 Loschmidt: „Constitutionsformeln der Organischen Chemie“
1873 van‘t Hoff, LeBel recognize the tetrahedral structure of carbon in its saturated compounds
– New ways to synthesize pure compounds, previously unknown compounds
– Directed synthesis and modification of structures
Friedrich August Kekulé von Stradonitz
(1829-1869)
Historic Milestones
1806 Morphine hypnotic 1875 Salicylic acid analgetic 1884 Cocaine anesthetic 1888 Phenacetine analgetic 1899 Acetylsalicylic acid analgetic 1903 Barbiturate sedative 1909 Arsphenamine antisyphilitic 1922 Insulin antidiabetic drug 1928 Penicillin antibiotic 1944 Streptomycin antibiotic 1945 Chloroquine antimalaria drug
Historic Milestones
1800 1850 1900 1950
1806 Morphine
1875 Salicylic acid
1884 Cocaine
1888 Phenacetine
1899 Acetylsalicylic acid
1903 Barbiturate
1909 Arsphenamine
1945 Chloroquine
1928 Penicillin
1944 Streptomycin
How Many Drugs Are There?
• Orange Book of the FDA (Food and Drug Administration) lists all approved medicines and their ingredients in the USA
• Orange Book 02/2004 – 10,565 approved medicines – 1,673 different drugs – About 500 drugs are protected by patents – About 300 drugs do not require prescription
(OTC – over-the-counter)
http://www.fda.gov
New Drugs – Germany
Pharmazeutische Zeitung: http://www.vfa.de/download/SAVE/en/statistics/innovation/vfastat-14-en-fa-mt-new-molecular-entities-in-germany.pdf?
Drug Design
How can we find NEW drugs? – Screening Search in plants, animals Prerequisite: good model system for rapid screening (in vitro, in vivo)
– Chance (serendipity) Looking for something completely different might reveal something new
– Rational design Understanding of the molecular mechanisms can guide the rational development of drugs
Serendipity
serendipity n: accidental sagacity; the faculty of making fortunate discoveries of things you were not looking for
Actually the term is derived from an old name for the island of Sri Lanka, Serendip. Horace Walpole wrote a story about three princes travelling around Serendip, finding marvellous things even though they weren't looking for them.
Webster‘s English Dictionary
Mauveine
• 1856: England is at war (crimean war) and has a huge need for quinine (antimalarial drug!)
• William Henry Perkin is given the task to develop a synthesis of quinine from coal tar
• All that was known of quinine was its chemical formula, not its structure – the use of structural formulas was introduced later!
William Henry Perkin
http://www.rpsgb.org.uk/members/museum/exhibition04/images/Henry_Perkin.jpg
Mauveine
• Oxidation of aniline with
potassium dichromate did not yield quinine
• Instead, he found mauveine,
the first synthetic organic dye!
• After Queen Victoria wore a mauveine-dyed silk gown for the first time, mauve was suddenly fashionable and Perkins became rather wealthy
www.spartacus.schoolnet.co.uk/ SCperkin.htm
Sulfanilamide
• Paul Ehrlich observed that some pathogens are selectively colored by azo dyes
• His idea to replace the azo group in azo dyes by toxic arsenic lead to the discovery of the first cure for syphilis (salvarsan) in 1909
Salvarsan
Sulfanilamide
• Since 1927 Gerhard Domagk headed the development of azo dyes as drugs at Bayer
• Most dyes did not show any effect in vitro
• Domagk introduced mice infected by streptococci as a model system
• Using this model, he identified the first effective dyes in 1932
• In 1935 these dyes were marketed successfully as antibiotics
http://www.britannica.com/nobel/micro/174_56.html
Gerhard Domagk
Sulfanilamide
• The dyes are not directly responsible for the effect
• Effect is caused by sulfanilamide, a metabolite • Sulfanilamide can replace p-aminobenzoic acid,
which is essential in bacterial metabolism
Testing Biol. Data
Drug Discovery Pipeline
Drug Design Accelerated by
Bioinformatics/Cheminformatics
Target ID Lead ID Optimization Approval
Real-World Pipeline
Jeffrey Augen: The evolving role of information technology in the drug discovery Process, Drug Discovery Today (2002), 7, 315
Pipeline
Biological Data – Selection of indication/disease to target
(mostly economic aspects) – Collection of all available biological and
clinical data – Ideal case: animal disease model available,
otherwise try to develop an animal model
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Target Identification (Target ID) – Target Discovery
• Search for possible molecular targets, i.e., molecules involved in the disease
• Identify the corresponding gene and protein
– Target Validation • Verify that the target is truly suitable as a target
– Is the protein expressed in the tissues in question? – Does a knock-out show an effect in the animal model?
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Lead Structure Identification (Lead ID) – Search for a lead structure (lead), i.e. a
small molecule binding to the selected target
– Methods: • High-throughput Screening (HTS) • Combinatorial Chemistry (Combichem) • Virtual Screening (vHTS)
– Also test some antitargets, i.e targets that should not be affected by the lead (remember ASA and COX-1/COX-2!)
Testing Biol. Data Target ID Lead ID Optimization Approval
Design Strategies
No target structure No known ligands
Combichem, HTS
Target structure No known ligands
De Novo Design
No target structure Ligands
Pharmacophores, Similarity, QSAR
Target structure Ligands
Structure-Based Design
Ligand-Based Structure-Based After: H. Kubinyi, Vorlesung Drug-Design
Testing Biol. Data Target ID Lead ID Optimization Approval
Dru
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Dru
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Pipeline
Lead Optimization
• Optimize lead to obtain a drug candidate – Improve response (µM ! nM)
– Reduce side effects
– Improve pharmacokinetics – Simplify synthesis
– Ensure patentability/absence of other IP
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Testing/Trials – Preclinical studies
Pharmcokinetics, pharmacodynamics (in animals) – Phase I clinical trials
First testing in (healthy) human subjects – Phase II clinical trials
100 – 500 patients – Phase III clinical trials
1000+ patients – Phase IV clinical trials
Long-term trials after approval
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Preclinical Studies • Formulation studies (solubility, particle size, etc.) • Studies on toxicity, effectiveness, pharmacokinetics
– Acute: ED50, LD50 – Usually done with rodents (rats, mice) – Test various dosage forms – Chronic toxicity (6 months) – Carcinogenity, teratogenity
• Duration: 2 – 3 years
After success in the preclinical studies: File application for clinical studies
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Phase I Clinical Trials
• Initial test in humans to assess safety of the candidate
• Trials usually include 20 – 100 healthy subjects
• Assess toxicity, pharmacokinetics
• Test for expected pharmacological effects
• Test for unexpected pharmacological effects (serendipity: sildenafil!)
• Derive suggestions for dosage
• Duration: 1+ years
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Phase II Clinical Trials • Studies include 100-500 in-patients • Assessment of dosage, efficacy, and safety of the drug • Development of dose-response relationships • Test for interactions with other drugs • Test for development of tolerance towards the drug • Most failures of drugs occur in phase II – usually the
drug turns out to be less effective than thought or demonstrates unexpected toxic effects
• Duration: 2+ years
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Phase III Clinical Trials • Biometric validation of efficacy and safety • 1000+ patients • Assess long-term safety • Multi-center study • Compare efficacy to placebo • Continue pharmacokinetic studies • Assess interaction with frequently prescribed drugs • Duration: 3+ years
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Approval • File application for approval in all relevant countries • Availability of drugs varies with the country
– Different standards for approval – Different economic potential – Different cultural background (example: Ru486!)
• Application for approval are files with the relevant national authorities (D: BfArM – Bundesinstitut für Arzneimittel und Medizinprodukte, USA: FDA – Food & Drug Administration)
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Phase IV Clinical Trials • Studies after the approval
• Assess safety and toxicity under therapeutic conditions and in large collectives
• Assess long-term effects
• Identify rare side effects and rare drug interactions (occurring less often than 1:1000)
• Compare risk/efficacy to other standard therapies
Testing Biol. Data Target ID Lead ID Optimization Approval
Pipeline
Development of one new drug: – Cost: $800 mio. (numbers from 2001) – Duration: 10-15 years
) Drug companies focus on block busters • Pharma industry limits research on areas with
high economic potential • Less promising areas (tropical medicine,
parasitology) have been reduced drastically over the last two decades (lack of market potential)
Testing Biol. Data Target ID Lead ID Optimization Approval
“Fail early, fail cheap” • Increasing R&D expenditures
seem to yield fewer and fewer NMEs (new molecular entities)
• Total cost increasing steadily: about $800 mio./NME
• Costs increase from early to late stages of the pipeline
• The later a candidate is taken out of development, the more money is lost
• Most candidates do fail! ) Rule of thumb:
“Fail early, fail cheap“ The Economist: http://www.economist.com/science/tq/displayStory.cfm?story_id=2477075
Sales
• Total pharma sales (2001): $ 225 billion • 70% of sales from
– Cardiovascular disease – Central nervous system – Metabolic disease – Respiratory disease – Antiinfectives
• Block buster Drugs with more than 1 billion USD in sales
• Megabrands Drugs with more than 1 billion USD per year
Where are the Targets? • Depends on the indication
– Infectious disease: in the pathogen – Carrier of the pathogen (Anopheles in case of malaria)
– In the human body (e.g., metabolic diseases)
• During the last decade, numerous medically relevant genomes have been sequenced – Human (2001) – Haemophilus influenzae (1996)
(respiratory infections, meningitis) – Helicobacter pylori (1997)
(gastric ulcers) – Plasmodium falciparum (2003)
(malaria)
• Target ID is a main driving force behind genome projects!
Targets Currently in Use
Hopkins, Groom: The druggable genome, Nat. Rev. Drug Discov. (2002), 1, 727-30
From Genomes to Targets
• Genome: 3 Gbp • 30 – 40k Gene • Known targets: 300 (?) • Each gene can code for
multiple proteins (< 10?) • 2001: Genome companies
turn into drug companies – Celera Genomics
acquires Axys Pharma – Human Genome Science
acquires Principia Pharma – ...
Venter et al., The sequence of the human genome, Science (2001), 291, 1304-51 Ann Thayer, Genomics moves on, Chemical & Engineering News (2001), 80, 25-36
Druggable Genome
• Number of targets in the human genome – Numbers as are usually wild guesses – 500 – 10,000 (?)
• A less wild guess is from Hopkins & Groom (2002) – A gene is druggable, if it is able to bind drug-like
ligands – An estimated 10 – 14% of human genes are
druggable – Only a small fraction of these are associated with
a disease
) Total number of targets: 600 – 1500
Hopkins, Groom: The druggable genome, Nat. Rev. Drug Discov. (2002), 1, 727-30
Design Strategies
No target structure No known ligands
Combichem, HTS
Target structure No known ligands
De Novo Design
No target structure Ligands
Pharmacophores, Similarity, QSAR
Target structure Ligands
Structure-Based Design
Ligand-Based Structure-Based After: H. Kubinyi, Vorlesung Drug-Design
Testing Biol. Data Target ID Lead ID Optimization Approval
Dru
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Effective Drugs For a compound to be an effective drug, it has to be
– Effective
The drug has to bind tightly to the receptor in concentrations that can be easily administered.
– Bioavailable
The drug has to be soluble, to be taken up by the body and needs to reach the target in sufficient concentration.
– Non-toxic
The drug should not show significant side effects and therapeutic doses should be significantly smaller than toxic ones.
– Cheap
The drug needs to be mass-producible, e.g., not to be extracted from a rare plant or animal.
Pharmacological Basics • Effect of a substance on the body
– Useful/desired – medicine – Harmful – poison
• Paracelsus: ‚the dose makes the poison‘! ) each drug has its own characteristic, dose-dependent effect
• Effect described by – Quality (type of effect) – Strength – Duration
• Effects are – Reversible or irreversible – Local or systemic (throughout the organism)
Pharmacological Basics
Interaction between drug and organism occurs in both directions – What does the organism do to the drug?
• Absorption (uptake) • Distribution, storage • Elimination (excretion and metabolism)
– What does the drug do to the organism? • Dose/concentration • Receptor response (susceptibility) • Non-receptor-mediated actions
Pharmaco- kinetics
Pharmaco- dynamics
Overview of the Different Areas
Application
Dissolution
Absorption
Distribution
Place of Action (Receptors)
Pharmacological Effect
Clinical Effect Toxic Effect
Storage
Biotransformation
Excretion
After: Mut, p. 5
Pharmaceutical Phase
Pharmacokinetic Phase
Pharmacodynamic Phase
Pharmacokinetics
Pharmaco- dynamics
Pharmaco- kinetics
Duration of Effect
Dose Intensity of Effect
Absorption
Distribution Excretion
Metabolism
Pharmacokinetics – Terms
• Concerned with understanding (and modeling) absorption, distribution, metabolism and excretion of a drug
(ADME – Absorption, Distribution, Metabolism, Elimination)
• Basics of pharmacokinetic models – Body modeled as consisting of individual compartments (e.g.,
blood, intestine, muscles)
– Central compartment: blood (plasma)
– Each compartment is characterized by • Drug concentration c
• Distribution volume v
– Drug can cross biological barriers from one compartment to another
– Simulation of these processes with methods from chemical kinetics
Absorption and Elimination
Central Compartment Biliary Renal
Gehirn
Blood-Brain Barrier
Enteral
Absorption
Elimination
Membranes of the GI Tract
Blood-Brain Barrier • Brain requires huge amounts of
nutrients and oxygen • Blood vessels in the brain are
surrounded by an additional cell layer, the blood-brain barrier (BBB)
• Protects the brain from toxins and infections
• Molecules larger than 400 – 500 Da cannot penetrate this layer
• Major hurdle to the delivery of drugs acting on the central nervous system (CNS)
http://institut.cochin.inserm.fr/research/scientific-departments/infectious-diseases/ team-p.o.-couraud/team-biology-of-brain-endothelium/images/Fig-1.gif
Blood vessels in the brain
Pharmacodynamics
Receptor theory
– Based on ideas of John Newport Langley (1852-1926), Paul Ehrlich (1854-1915), Alfred Joseph Clark (1885-1951)
– Drugs bind to ‘receptors‘ to cause an effect
– In general, receptors are places where the action occurs; mostly they are proteins, though
– Binding of the drug to the receptor is usually reversible and then follows mass action
Dose-Response Relationships
• „The dose makes the poison“ – Typically, an increase in dose
increases the effect – Above a certain dose, additional
toxic effects may be observed – Strength/duration of effect depends
on many factors, e.g., genotype, age, body mass, …
– Difficult to quantify strength of the response
) Dose-response relationships
measured for collectives – ED50: median effective dose – LD50: median lethal dose
Katzung, Basisc and Clinical Pharmacology, p. 30
Therapeutic Index
Mut, S. 81
ED50 LD50
• Therapeutic index or therapeutic ratio is the ratio between the concentration causing a toxic effect and the concentration causing a therapeutic effect
• It is a measure of a drug‘s safety
Pharmacodynamics
• Effect: – Intensity – Duration
• MEC: minimal effective concentration
• MTC: minimal toxic concentration
MTC
MEC
Intensity
Time
Duration Pl
asm
a co
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Summary
• In the middle of the 19th century progress in chemistry
allows the production of pure drugs
• Serendipity governs the drug discovery process until
the middle of the 20th century
• The number of (human) drug targets is rather limited
(probably in the hundreds)
• Successful drugs need to fulfill several requirements
– Effectiveness
– Bioavailability
– Low toxicity/side effects