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An introduction to in silico methods in drug discovery, covering small molecule drugs and biologics, and considering safety and efficacy.
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An Intro to in silico drug Design:considering safety and efficacy
Dr Lee [email protected]
Lecture Aim
This lecture aims to provide a basic understanding of the concept of protein and molecular in silico engineering/design as part of the drug development process:-
Introducing theory and approaches, drivers, databases and software – and with a focus on safety and efficacy.
This Lecture Covers
• Drivers for use of computational approaches
• Small molecule drugs• Getting protein structures• Simulation of molecular interactions• Considering safety during design
• Biologics – antibody therapeutics• Engineering biologics for safety – reducing immunogenicity• Considering efficacy of biologics
• We will also highlight key software or data sources along the way
Key Drivers for in silico
Business
Target identification
Lead selection
Lead refinement
Pre-Clinical phases
GenomicsProteomics/MetabolomicsInteraction Networks
Molecular modellingProtein modellingChemoinformatics
Molecular modellingData modellingInteraction Networks
Systems BiologyIn vitroIn vivo
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Ethics Drivers• Use of animals in research
• 3Rs – Refine, Reduce, Replace
• Relevance of animal data for human use• Extrapolation across species
• Improvement of safety for subsequent trials
• Regulatory requirements and change
Extrapolation of data across speciesHow relevant is animal physiology to human physiology ?
Models not available for all diseases
Choice of species can be important• 30% attrition due to no efficacy in man• 10% attrition due to toxicity
For biologics, even more difficult to predict
Part 1: Small Molecule Drugs
8
Safety and Efficacy of Small Molecule Drugs
• Safety: safety issues primarily focus on the potential of the small molecule to have off-target effects, metabolite/breakdown product toxicity, or buildup/non clearance
• Efficacy: efficacy issues focus on bioavailability and good binding kinetics to the right target protein – including variations of that protein (SNPs/mutants)
1st we need a source of molecules: Chemical Repositories
• Databases with safety information (GRS, CAS)
• Databases with structure and vendor/price – individual chemical supply companies - Zinc
• Databases with multiple information types – ChEMBLdb, PubChem, Kegg
ChEMBLdb“The ChEMBL database (ChEMBLdb) contains medicinal chemistry bioassay data, integrated from a wide variety of sources (the literature, deposited data sets, other bioassay databases). Subsets of ChEMBLdb, relating to particular target classes, or disease areas, are exported to smaller databases, These separate data sets, and the entire ChEMBLdb, are available either via ftp downloads, or via bespoke query interfaces, tailored to the requirements of the scientific communities with a specific interest in these research areas”
• Targets: 10,579• Compound records: 1,638,394• Distinct compounds: 1,411,786• Activities: 12,843,338• Publications: 57,156
(release 19)
ChEMBLwww.ebi.ac.uk/chembl/
What can we do with chemical models?
We can investigate structure and similarities of structure between molecules
We can map structural characteristics to properties (SARs)
We can study molecular interactions – particularly with proteins
• Computation to assess binding affinity
• Looks for conformational and electrostatic "fit" between proteins and other molecules
• Optimization: Does position and orientation of the two molecules minimise the total energy? (Computationally intensive)
• Docking small ligands to proteins is a way to find potential drugs. Industrially important!
Interactions – Docking & Screening
• Docking small ligands to proteins is a way to find potential drugs. Industrially important
• A small region of interest (pharmacophore) can be identified, reducing computation
• Empirical scoring functions are not universal
• Various search methods:• Rigid- provides score for whole ligand (accurate)• Flexible- breaks ligands into pieces and docks them
individually
Virtual Screening
So – we need protein (target) structures
http://www.rcsb.org/
The PDB
The PDB was established in 1971 at Brookhaven National Laboratory and originally contained 7 structures. In 1998, the Research Collaboratory for Structural Bioinformatics (RCSB) became responsible for the management of the PDB.
Last year (2013), 9597 structures were deposited from scientists all over the world – this year (2014) so far, 8391
Now totals 104,866 (yesterday) structures
Entries in database - cumulative and by year
Red = total
Blue = yearly
What if there is no structure available?Can we predict structures?
Tertiary structure is dependent on ‘folding’ of the protein.
Recognition, characterisation, and assignment of domains and folds is a major area of structural bioinformatics.
Predicting structure from sequence is one of the biggest challenges...
Levinthal’s paradox (1969)
100 residues = 99 peptide bonds
therefore 198 different phi and psi bond angles
3 stable conformations of bond angle = 3198 possible conformations
At a nano/pico second sample rate proteins would not find correct structure for a long time (longer than the age of the Universe!)
Folding is Complex: Is a truly random approach possible?
Proteins fold on a milli/micro second timescale – this is the paradox...
phi
psi
1. proteins do NOT fold from random conformations, which was an assumption of Levinthal's calculation
2. instead, they fold from denatured states that retain substantial 2o, and possibly 3o, structure
• Simulations are computational expensive• Gross approximations in simulations• Nature uses tricks such as
• Posttranslational processing • Chaperones• Environment change
Why are folding simulations so difficult?
How does it work at all?
Complexity & Diversity – potential vs reality
If the average protein contains about 300 amino acids, then there could be a possible 20300 different proteins
(Apparently) this is more than the atoms in the universe!
Yet a human (complex) has only 30,000 proteins
All proteins so far appear to be represented by between 1000 - 5000 fold types
Two reasons for limited fold space
Convergent evolution
Certain folds are biophysically favourable and may have arisen in multiple cases
Divergent evolution
The number of folds seen is limited because they have evolved from a limited number of common ancestor proteins
Despite the evolutionary limitation of the number of existing folds (fold space) it is still complex enough to make classification and
comprehension difficult
Why is Folding Difficult to do?
It's amazing that not only do proteins self-assemble -- fold -- but they do so amazingly quickly: some as fast as a millionth of a second. While this time is very fast on a person's timescale, it's remarkably long for computers to simulate.
In fact, it takes about a day to simulate a nanosecond (1/1,000,000,000 of a second) of dynamics for a reasonable sized protein. (eg Intel core i7 2.66Ghz)
Unfortunately, proteins fold on the tens of microsecond timescale (10,000 nanoseconds). Thus, it would take 10,000 CPU days to simulate folding -- i.e. it would take 30 CPU years! That's a long time to wait for one result!
A compromise: Homology modelling
If there is no structure for your protein - perhaps there is one for a similar protein.
Sequence alignment tools can be used to compare this to your sequence with unknown structure
Homology searching and sequence alignment is now the first step to protein structure prediction
If homologous proteins are found with structures, unknown can be ‘overlayed’ and structure inferred
Homology Modeling
Based on two assumptions:
1.The structure of a protein is determined by its amino acid sequence alone
2.With evolution, the structure changes more slowly than the sequence - similar sequences may adopt the same structure
Sequence alignment
TEX19 – human protein without a structure.
PDB 2AAM: Crystal structure of a putative glycosidase (tm1410) from thermotoga maritima
Structure inference/alignment
ExPASy - SwissModelSwissModel (swissmodel.expasy.org/)
Phyre2http://www.sbg.bio.ic.ac.uk/phyre2
More annotation http://genome3d.eu/
Using the Models – Docking/Screening
• Choose and prepare target protein• Identify binding pocket• Fit ligand to pocket• Score
• (for screening – repeat!)
Identify the Binding Pocket
• Could identify this by the location of an existing co-crystallised ligand
• Or use surface sphere clusters• Or identify it by clustering of solvent molecules (normally
water)• Perhaps identify it by clustering of fragments (SurFlex
dock protomol)
Binding site based on existing ligand
• Most methods allow you to specify where the site is – perhaps by identifying key residues or based on an existing ligand
• Could use the ‘hole’ left by the ligand as a pocket, or use the ‘surface’ of the ligand as a protomol
Surface Sphere generation• Generate the surface of the target
– Connolly surface
• ‘Rolls’ a sphere the radius of water across the van der Waal’s surface of the target
• Each atom’s centre of van der Waal’s radius acts as a sitepoint for the generation of a sphere on the surface whose centre is perpendicular to the surface at the sitepoint.
• Spheres are then clustered – each cluster is a potential pocket
Identified pocket
Prepare the ligand
• The ligand needs to be prepared too• Drawn & minimised• From a database - & minimised• Extracted from another/the same binding site
• Hydrogens added etc• Minimised/optimised – ready to dock
Docking
• Rigid docking -> ligand is fixed conformationally
• Flexible docking –> ligand is conformationally flexible
• Posable -> ligand is rigid, but moved spacially
Rigid Ligand docking• Centres of spheres
representing the binding pocket act as ‘Site Points’
• The atoms of the ligand are matched to the site points
• Once orientation made, possibly interaction minimised: receptor kept rigid and ligand flexible
Alternatives
Flexible Docking Posable Docking
Rings treated as flexible
Other bonds treated as flexible/rotamers
Rings treated as rigid – ligand fragmented
Rigid docking, but ligands posed conformationally
•Rotated•Twisted•Flipped etc
And repetitively docked to find best fit
Example Interaction – Avidin / Biotin
Virtual Screening• Docking – but repeated with many potential ligands
• Libraries can come from resources such as PubChem/ChEMBLdb – vendors – or other in-house sources
• From specialised databases holding structures suitable for docking
• It is important to have a diversified library especially for rigid docking !
Considering safety & efficacy – “Drug-like”
Lipinski rule of 5 (or Pfizer rule)
‘Compounds which violate at least two of the following conditions have a very low chance of being orally bioavailable’
• MW <500 Da• log P (lipophilicity) <5• number of H bond donors <5• number of H bond acceptors <10
Works well once you have descriptions of small molecules – can be search criteria in databases...
ADME / ADME-Tox• Lipinski rule is really the 1st step in ADME (adsorption,
distribution, metabolism, excretion) modelling
• Structure Activity Relationships (SARs) – similar molecules will behave in similar ways, ie have similar effects.
• Allows for knowledge-based compariative analysis – Tox databases
ChEMBL SARfari(s)
Knowledge-based tox in silico
www.dixa-fp7.eu
Toxicogenomics – Open TG-Gates
HeCaToS http://www.hecatos.eu/
Part 2: Biologics
What are Biologics?
Typically biologics are thought of as being either antibody therapeutics or components of vaccine products.
However... (from FDA CBER)
Biological products include a wide range of products such as vaccines, blood and blood components, allergenics, somatic cells, gene therapy, tissues, and recombinant therapeutic proteins. Biologics can be composed of sugars, proteins, or nucleic acids or complex combinations of these substances, or may be living entities such as cells and tissues. Biologics are isolated from a variety of natural sources - human, animal, or microorganism - and may be produced by biotechnology methods and other cutting-edge technologies. Gene-based and cellular biologics, for example, often are at the forefront of biomedical research, and may be used to treat a variety of medical conditions for which no other treatments are available.
Center for biologics evaluation and research
We will just consider antibodies here...
Safety and Efficacy of Biologics
• Safety: safety issues primarily focus on the potential of the protein biologic to raise an immune response in the subject. This could be mild or severe.
• Efficacy: efficacy issues focus on either the raising of anti-drug antibody responses, or the in vivo half life of the protein
Making suitable Abs for therapy
Monoclonal antibodies are traditionally made using Mice* – these are fine for R&D use, but bring problems for use in Humans
When developing Abs for therapeutic use there are very few requirements for modelling or in silico engineering as most of the work can be simple molecular biology (gene editing/expression systems)
However, the use of in silico engineering provides further options for improving or modifying function – particularly considering safety and efficacy.
*also phage or ribosome display – or now, humanised mice, which can avoid these problems – but are beyond the scope here
Immune response: B-cell activation
a) "B cell activation" by Fred the Oysteri. Licensed under Public domain via Wikimedia Commonsb) "T-dependent B cell activation" by Altaileopard - Own work. Licensed under Public domain via Wikimedia Commons
(a)
(b)
Antibody structure
By Dan1gia2 (Own work) [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
Size relationship
antibody
rhinovirus
DNA and DNA polymerase
ribosome
rhodopsin
membrane
cyclooxygenase
http://www.rcsb.org/
Chimeric Ab:
Retain the murine variable domains – splice to Human constant domain.
75% Human*
Humanised Ab:
Retain the murine CDRs – splice to Human variable framework & constant domain.
95% Human*
Best to try and ‘humanise’ them as a first step – helps both:
Safety and Efficacy
Engineering:* refers to percentage Human origin. Of course, being both mammals the mouse and Human have fairly high antibody sequence similarity
Targets for engineering
By Dan1gia2 (Own work) [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
CDR – tweak to remove unwanted PTM sites – mitigate immunogenicity (more later) at human/mouse interface
VL/H – remove unwanted PTMs. If Chimeric, reduce immunogenicity at C/V interface
Fc – Select effector functions, remove unwanted PTMs, enhance function?
Other – Add drug conjugates?(Beyond the scope of this talk)
What about Fc selections?
Salfeld, J.G., 2007. Isotype selection in antibody engineering. Nature Biotechnology, 25(12), pp.1369-1372.
Half life
• Proteins & Biologics will be slowly cleared by the system (either immunologic response or cellular uptake/destruction)
• Two main strategies to increase serum halflife: increase the size (pegylation) or exploit (enhance?) natural protein recycling (via FcRn)
FcRn – neonatal Fc Receptor
Roopenian, D.C. & Akilesh, S., 2007. FcRn : the neonatal Fc receptor comes of age. Nature Reviews, Immunology, 7, pp.715-725.
FcRn in the adult
Roopenian, D.C. & Akilesh, S., 2007. FcRn : the neonatal Fc receptor comes of age. Nature Reviews, Immunology, 7, pp.715-725.
IgG : FcRn binding
Roopenian, D.C. & Akilesh, S., 2007. FcRn : the neonatal Fc receptor comes of age. Nature Reviews, Immunology, 7, pp.715-725.
Deimmunisation & ADA• If part of the Ab is recognised as foreign – it can stimulate
a T-cell response when the fragment is presented on MHCII, and...
• If the Ab contains a B-cell epitope (it will), then...
• The immune system will raise antibodies to the biologic which may be harmful to the patient or at least reduce the usefulness of the drug
• Engineer to remove the T-cell epitopes (Humanisation + deimmunisation strategy)
Safety: reducing immunogenicity
a) "T-dependent B cell activation" by Altaileopard - Own work. Licensed under Public domain via Wikimedia Commons
(a)
If the Antibody (antigen) doesn’t have any epitopes that will (a) bind MHC II or (b) be recognised by a TCR – the B-cell will not be activated, and no ADA
We can deal with (a) though engineering - deimmunisation
Predicting T-cell epitopes http://www.iedb.org/
Sequence-level engineering
PGLVRPSQTLSLTCT = T-cell epitope
PGLVRPSATLSLTCT = weak or non-epitope?
Remove or mitigate the risk – taking into account the promiscuity of the epitope for HLA types, and population variation.
MHCII varies by population, but so does IgG...
Jefferis, R. & Lefranc, M.-paule, 2009. Human immunoglobulin allotypes. Possible implications for immunogenicity. mAbs, 1(4), pp.1-7.
Aggregation & ADAT
-cel
l epi
tope
sA
ggregatio n
a) "B cell activation" by Fred the Oysteri. Licensed under Public domain via Wikimedia Commons
(a)If antigen can cross-link the B-cell receptor, the cell will become activated without the presence of a T-cell
The result is mainly IgM, but can still be a problematic response
Aggregated antigen can cause the cross-linking – even when as “Human-like” as possible
This is T-cell Independent B-cell Activation
Aggregation & ADA
Engineer to remove potential aggregation hotspots (disorder/hydrophobicity, PTMs and pI shift potential, hydrophobic patches)
Predicting aggregation is really hard!
Problem – sometimes this is due to formulation!
Final Comments
Remember the Key Drivers for in silico approaches
Explore the following Software ToolsAs well as resources mentioned in the slides!
Homology ModellingModeller, Phyre, SwissModel
Model ViewersPymol, Jmol, Rasmol
Molecular Simulation etcGromacs, Tinker, Amber, NAMD, Charmm,
Docking/ScreeningSurflex Dock, Dock, AutoDock, Vina
Graphical Tools/builders/interfacesChimera, Maestro, Ghemical, VMD, DeepView
Suites (companies)Tripos, Accellrys, OpenEye, ChemAxon, Schrodinger, MoE, Yasara
Some are free for academic use, but cost for commercial use
Take note and beware!
Workflow example – free vs paid
ChEMBL
PDB
Discovery Studio
Marvin Sketch
Chimera
Gromacs
Dock
Chimera
ligand
target
get structures
minimisation
dynamics
docking
evaluation
preparation
Commercial suite vs free tools
£££ $$$