Computer-aided drug design – the next twenty years John H. Van Drie Novartis Institutes for BioMedical Research Cambridge, MA A talk in commemoration of

Computer-aided drug design – the next twenty

years

John H. Van Drie

Novartis Institutes for BioMedical Research

Cambridge, MA

A talk in commemoration of Yvonne C. Martin, given at the ACS session Mar 2007 in honor of

her ‘retirement’.

2 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007

Hugo, YCM, and Han


Why 20 years? Predicting the future??

This commemorates work that Yvonne and I and many others at Abbott did twenty years ago, the first successful virtual screen (a pharmacophore search of a 3D database, which yielded a novel D1 agonist)1,2,3.

“Predictions are hard…esp. about the future” – Yogi Berra

Nonetheless, I think it’s safe to predict that

At the 2027 CADD Gordon Conference, we’ll hear a talk on

““Progress in scoring functions”Progress in scoring functions”

1 JH Van Drie, D Weininger, YC Martin, JCAMD, 1989; 2 JW Kebabian et al, Am J Hypertens. 1990 3:40S2 YC Martin, JMC, 1992. The events described therein took place in the summer of 1987.

And at the 2028 Comp. Chem. Gordon Conference, we’ll hear

““Progress in polarizable force-fields”Progress in polarizable force-fields”


Yvonne’s compatriot Peter Goodford concluded his conference on computational drug design in Erice, Sicily in 1989 with a list of things we had to work on. This list looks very modern, e.g. “we must improve homology modelling”, “we must predict solubility”.

Today, I’m really not trying to predict the future. My aspiration is to provoke some thinking in all of you about where our field is heading (as Yvonne has so often done herself).

Why 20 years? Predicting the future??


All new technologies tend to follow a similar path

Depth ofDepth ofCynicismCynicism

OverreactionOverreactionto Immatureto ImmatureTechnologyTechnology

NaiveNaiveEuphoriaEuphoria

EX

PEC

TA

TIO

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True UserTrue UserBenefitsBenefits

AsymptoteAsymptoteof Realityof Reality

Peak ofPeak ofHypeHype

J. Bezdek, J. Bezdek, IEEE Trans.IEEE Trans.Fuzzy SysFuzzy Sys., ., 11, 1-5 (1993) , 1-5 (1993) His His figure put into PPT by J. D. Bakerfigure put into PPT by J. D. Baker

TIMETIME


In CADD, one can put dates on each of these turns

Depth of Cynicism – Depth of Cynicism – 1994-6, 1994-6, the era of “make ‘em the era of “make ‘em all, let the assay sort all, let the assay sort ‘em out”‘em out”

OverreactionOverreactionto Immatureto ImmatureTechnologyTechnology

Oct 5, 1981Oct 5, 1981

EX

PEC

TA

TIO

NEX

PEC

TA

TIO

N

True UserTrue UserBenefits – 2000 and beyondBenefits – 2000 and beyond

Peak of Hype – 1989-Peak of Hype – 1989-1991 “we can design 1991 “we can design drugs atom-by-atom”drugs atom-by-atom”

TIMETIME

Designing drugs by computer at MerckAlso, P Gund et al, Science, 1980


Depth of Cynicism – Depth of Cynicism – 1994-6, 1994-6, the era of “make ‘em the era of “make ‘em all, let the assay sort all, let the assay sort ‘em out”‘em out”

19801980

Peak of Hype – 1989-Peak of Hype – 1989-1991 “we can design 1991 “we can design drugs atom-by-atom”drugs atom-by-atom”

TIMETIME

But Yvonne was at work on QSAR far before 1980…

EX

PEC

TA

TIO

NEX

PEC

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Corwin Corwin Hansch Hansch devises devises QSARQSAR

19601960

Yvonne Yvonne begins begins working working w/ w/ CorwinCorwin

Yvonne publishes Yvonne publishes Quantitative Drug DesignQuantitative Drug Design

2001 - QSAR GRC 2001 - QSAR GRC becomes CADD becomes CADD GRCGRC

Yvonne Yvonne chairs 2chairs 2ndnd QSAR QSAR Gordon Gordon ConferenceConference


19871987

EX

PEC

TA

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PEC

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TIMETIME

This forms the basis for my main projection for the future of CADD – this has been only a warmup

20072007

Dramatically Dramatically higher higher expectationsexpectations

I can’t say when the new I can’t say when the new wave will begin, nor can I wave will begin, nor can I imagine what will be the imagine what will be the stimulus to kick it offstimulus to kick it off

20272027


The key drivers of the evolution of CADD

CADD is emerging as a sub-discipline of computational chemistry, distinct in its own right.

Computational chemistry itself is an off-shoot of physical chemistry, sharing its paradigm of aiming to achieve atomic-level understanding of experimental phenomena.

Like comp. chem., CADD aims to present explanations of experimental phenomena, but in addition aims to provide answers to the fundamental question of medicinal chemistry:

What molecule(s) should be made next?

This leads to things like virtual screening, virtual library design, de novo design, etc. – heresy to many academic comp. chemists.


The key drivers of the evolution of CADD

CADD focuses on the design and discovery of ligands and drugs.

To design a potent ligand, “all” it takes is:

(1) To understand molecular recognition, and

(2) To exploit that understanding in proposing new molecules to make.

Our understanding, #1, is astonishingly primitive, and #2 works best today in lead discovery (where lots of options are available, and lots of predictions are tested), less well in lead optimization.

However, recall too

“It’s relatively easy to discover a potent ligand,

it’s damned tough to discover a drug” – E. H. Cordes


Outlook #1: To gain a more accurate understanding of molecular recognition…

We’ve relied too long on molecular dynamics (MD) to handle thermodynamics of ligand-protein interactions, e.g. free-energy perturbation. The results have fallen short of our high hopes.

At a fundamental level, ligand-receptor interactions often display non-additivity. Yet, almost all of our energy functions1 and scoring functions2 are linear, i.e. implicitly assume additivity:

However, many structure-activity relationships display non-additivity, like this Raf kinase SAR, that led to sorafenib3:

1 CHARMm force field; 2 HJ Böhm , JCAMD, 1994 3 RA Smith et al, BMCL, 2001

SNH

NH

O

ON

ONH

NH

O

O17 uM

0.54 uM


Outlook #1: …we’ll finally need to learn thermodynamics

A proper thermodynamic treatment naturally leads to a description of non-additivity. 1,2

One area in a hot ‘naïve euphoria’ phase are methods for treating thermodynamics of ligand-protein interactions better:

- Gibbs’ ensemble methods used by LOCUS (F. Guarnieri originally), and related things at other companies (Bioleap, Vitae, SolMap). Stems from work of M Mezei at Mt Sinai and others.

- Internal coordinate methods (R. Abagyan, M. Jacobson) allow greatly increased sampling vis-à-vis MD.

- Ken Dill, Rob Phillips et al. published in 2006 new equations for statistical dynamics of non-equilibrium systems (“principle of maximum caliper”, Am J Phys, 74:123, 2006) – a bolt of lightning with as yet no thunder.

1 K. Dill, JBC, 1997; 2 JH Van Drie, manuscript sitting on my desk for years


Outlook #2: We’ll get much better at understanding what it takes to turn a potent ligand into a drug

The attrition rates of drug candidates in clinical trials are staggering – we’re throwing lots of money down the drain, and, more importantly, the fruits of peoples’ creativity.

The more that we understand why molecules fail, the better we’ll be able to design molecules that don’t.

This is the grand challenge of drug design in the next 20 years.

See, for example, S. Biller et al, “The Challenge of Quality in Candidate Optimization, in Borchardt RT, eds. Pharmaceutical Profiling in Drug Discovery for Lead Selection, 2004. Figure from Kola & Landis, Nat Rev Drug Disc, 2004


The best example of our recently-increased understanding of a liability: hERG and long QT

Outlook #2.1: We’ll see a lot more of this type of stuff.

R. A. Pearlstein et al, BMCL, 2003

We now have atomic-level understanding of binding to the hERG channel, mediator of the clinical LQT syndrome



However, we don’t need to model all liabilities at the molecular level We have tons of data, and are getting more. We tend not to use it outside the chemical series for which it was developed.

Outlook #2.2: our methods for computationally learning from data will get much better (they stink now). People thought SVM’s would be our salvation – hasn’t happened.

Outlook #2.3: we’ll get much better at building empirical 3D models. Something will come along to replace CoMFA/CoMSiA, and better alignments will arise via improved pharmacophore methods.

PubMed articles with 'pharmacophore' in title

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50

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1970 1980 1990 2000 2010

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Outlook #2.4: Use of pharmacophores will grow. The science is there to create simple pharmacophore models of each receptor-mediated liability for which in vitro data is available (e.g. off-target GPCR’s). We need to just do it.

Outlook #2.5 We’ll be able to find the data we need.

Figure from JH Van Drie, IEJMD, 2007



Outlook #3: We’ll be led into new classes of drug targets – ones that challenge our competencies

Protein-protein interactions (PPI’s) are thought to be a nearly-impossible challenge as drug targets.

Yet, we’re starting to crack them.

If one figures that there’s ~30,000 genes in the genome, that gives us ~30K protein targets, but 30K x 30K = ~ 1 billion PPI pairs as targets. Lots of opportunity, once we figure out how to wrestle these to the ground.

This shows Novartis’ success in designing inhibitors to IAP, mimicking part of the SMAC interaction partner. (C. Straub, Keystone Symposium April 2006).


Outlook #4: wild idea: self-assembling drugs

Exjade is a new Novartis drug for iron chelation therapy. Two divalent molecules together form a tetravalent complex of iron.

K. Rajangam et al & S. Stupp, Nano Lett, 2006; GW comment made at MIT-Novartis Nanotechnology Symposium, Nov, 2006

Note how this allows us to design small molecules to slip across the gut wall, but to reassemble to bigger things at the site of action.

To design these, we must understand thermodynamics (G. Whitesides).

S. Stupp et al at Northwestern are investigating something even more bizarre: molecules that self-assemble around a blood vessel to promote neovascularization:


Outlook #5: pathways and “systems biology” – it’s not enough to think about inhibiting one target

Our target selection must take into account the entire signalling network or pathway. For example, the cellular phenotypes of inhibiting each of these kinases in the same pathway is totally different

MAP MEK ERK

Most of this modelling up to now has been done by analogy to electrical circuits, i.e. numerically solving coupled ordinary differential equations. But does the proper approach reside here?

A Perelson, et al, “HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span,

and viral generation time.”, Science, 1996.

A breakthrough in our understanding of how HIV causes AIDS came from mathemetical modelling of the entire system


Outlook #6: Things that require an expert today will be on chemists’ desktops tomorrowIt’s mainly an issue of building intuitive user interfaces.

We tried this with Catalyst (1990-1994) but failed.

At Novartis, we’re putting sophisticated methods on chemists’ desks, called FOCUS.

Also, “the slow one now will later be fast…” – Bob Dylan, 1964


Outlook #7: Virtual screening will become routine

I anticipate that virtual screening will become as routine as HTS is now.

The driver of that will be the growing appreciation of the importance of speed. VS can provide a chemical starting point relatively quickly. HTS is more comprehensive, but when all the assay-reformatting, etc. is accounted for, it takes much longer.

It’s quite a surprise that it’s still relatively rare in Big Pharma, despite it having been introduced 20 years ago.

For a recent overview of methods and applications, see Shoichet & Alvarez, Virtual Screening, 2005


In summary, these are my outlooks for the next 20 years in CADD

1. “Computational thermodynamics” will flower

2. Increased ability to turn a potent molecule into a drug

i. Use molecular understanding for receptor-mediated off-target liabilities, e.g. hERG

ii. Our computer learning methods will greatly improve, to allow us to build good empirical models

iii. We’ll get much better at building empirical 3D models

iv. We’ll have at least pharmacophore models of each receptor-mediated off-target liability

v. We’ll be able to find the data we need.


In summary, these are my outlooks for the next 20 years in CADD (cont’d)

3. We’ll conquer challenging new classes of drug targets, e.g. PPI’s

4. We’ll learn to design self-assembling drugs

5. We’ll use our knowledge of pathways to predict which targets provide the best intervention point

6. Sophisticated CADD methods will be on the desktops of medicinal chemists. What is fancy today will be routine tomorrow.

7. Virtual screening will become routine.


My penultimate comment is to echo Peter Goodford


And, finally…

Thanks, Yvonne, for introducing me to such an endlessly fascinating line of work.

Documents

Computer-aided drug design – the next twenty years John H. Van Drie Novartis Institutes for BioMedical Research Cambridge, MA A talk in commemoration of