An Intro to in silico drug Design:considering safety and efficacy

Dr Lee Larcombeleelarcombe@gmail.com

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

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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

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