Novartis Template 2003 - acscinf.org

Novartis Template 2003 - acscinf.org

Computer-aided drug design the next twenty years A talk in commemoration of Yvonne C. Martin, given at the ACS session Mar 2007 in honor of her retirement. John H. Van Drie Novartis Institutes for BioMedical Research Cambridge, MA Hugo, YCM, and Han / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 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 its safe to predict that At the 2027 CADD Gordon Conference, well hear a

talk on Progress in scoring functions And at the 2028 Comp. Chem. Gordon Conference, well hear Progress in polarizable force-fields 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. 1 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Why 20 years? Predicting the future?? Yvonnes 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.

/ 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Today, Im really not trying to predict the future. My aspiration is to provoke some thinking in all of you about where our field is EXPECTATION All new technologies tend to follow a similar path Peak of Hype Asymptote of Reality Naive Euphoria True User Benefits

Overreaction to Immature Technology TIME Depth of Cynicism / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 J. Bezdek, IEEE Trans. Fuzzy Sys., 1, 1-5 (1993) His figure put into PPT by J. D. Baker In CADD, one can put dates on each of these turns EXPECTATION Peak of Hype 19891991 we can design drugs atom-by-atom True User Benefits 2000 and beyond Oct 5, 1981 Overreaction

to Immature Technology Designing drugs by computer at Merck Also, P Gund et al, Science, 1980 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 TIME Depth of Cynicism 1994-6, the era of make em all, let the assay sort EXPECTATION But Yvonne was at work on QSAR far before 1980 1960 Yvonne Peak of Hype 1989begins 1991 we can design working drugs atom-by-atom w/

Corwin Yvonne chairs 2nd QSAR Gordon Conference Yvonne publishes Quantitative Drug Design 1980 Corwin Hansch devises QSAR / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 2001 - QSAR GRC becomes CADD GRC TIME Depth of Cynicism 1994-6,

the era of make em all, let the assay sort EXPECTATION This forms the basis for my main projection for the future of CADD this has been only a warmup Dramatically higher expectations 1987 2007 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 TIME 2027 I cant say when the new wave will begin, nor can I imagine what will be the

stimulus to kick it off 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 design, etc. heresy to many / 24 CADD: the next twenty years / J. H. Van Drie novo / Mar 25, 2007

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 Its relatively easy to discover a potent ligand, its damned tough to discover a drug E. H. Cordes 0 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Outlook #1: To gain a more accurate understanding of molecular recognition Weve 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 nonadditivity, like this Raf kinase SAR, that led to sorafenib3: O N S H N O H N H N H N O O 0.54 uM

O 17 uM 1 CHARMm force field; 1 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 2 HJ Bhm , JCAMD, 1994 3 RA Smith et al, BMCL, 2001 Outlook #1: well finally need to learn thermodynamics A proper thermodynamic treatment naturally leads to a description of non-additivity. 1,2 One area in a hot nave 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--vis MD. - Ken Dill, Rob Phillips et al. published in 2006 new equations for statistical dynamics of non-equilibrium 1 K. Dill, JBC, 1997; 2 JH Van Drie, manuscript sitting on my desk for years systems (principle of maximum caliper, Am J Phys, 2 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 74:123, 2006) a bolt of lightning with as yet no Outlook #2: Well 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 were 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 well be able to design molecules that dont. This is the grand challenge of See, for example, S. Biller et al, The Challenge of Quality in Candidate Optimization, in Borchardt RT, drug design inFigure thefrom next eds. Pharmaceutical Profiling in Drug Discovery for Lead Selection, 2004. Kola & 20 Landis, Nat Rev Drug Disc, 2004

years. 3 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Outlook #2: Well get much better at understanding what it takes to turn a potent ligand into a drug The best example of our recently-increased understanding of a liability: hERG and long QT We now have atomic-level understandi ng of binding to the hERG channel, mediator of the clinical LQT Outlook #2.1: Well see a lot more of this type of stuff. syndrome R. A. Pearlstein et al, BMCL, 2003

4 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Outlook #2: Well get much better at understanding what it takes to turn a potent ligand into a drug However, we dont 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 SVMs would be our salvation hasnt happened. Outlook #2.3: well get much better at building empirical 3D models. Something will come along to replace CoMFA/ CoMSiA, #2.4: and better alignments will arise via improved Outlook Use of pharmacophores will pharmacophore methods.

grow. The science is there to create simple PubMed articles with 'pharmacophore' in title 300 number of articles pharmacophore models of each receptormediated liability for which in vitro data is available (e.g. off-target GPCRs). We need to just do it. 250 200 150 100 50 0 1970 1980

1990 year JH Van Drie,we IEJMD, 2007 Outlook #2.5 Well be able toFigure findfrom the data 5 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 2000 2010 Outlook #3: Well be led into new classes of drug targets ones that challenge our competencies Protein-protein interactions (PPIs) are thought to be a nearly-impossible challenge as drug targets. Yet, were starting to crack them. This shows Novartis

success in designing inhibitors to IAP, mimicking part of the SMAC interaction partner. (C. Straub, Keystone Symposium April 2006). If one figures that theres ~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. 6 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 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. S. Stupp et al at Northwestern are investigating something even more bizarre: molecules that selfassemble around a blood vessel to promote neovascularization: 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). K. Rajangam et al & S. Stupp, Nano Lett, 2006; GW comment made at MIT-Novartis Nanotechnology Symposium, Nov, 2006 7 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 Outlook #5: pathways and systems biology its not enough to think about inhibiting one target A breakthrough in our understanding of how HIV causes AIDS came from mathemetical modelling of the entire system A Perelson, et al, HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time., Science, 1996. 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. does the proper 8 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, But 2007 Outlook #6: Things that require an expert today will be on chemists desktops tomorrow Its mainly an issue of building intuitive user interfaces. We tried

this with Catalyst (19901994) but failed. At Novartis, were putting Also, the slow one now will later be fast Bob sophisticat Dylan, 1964 9 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 ed methods 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. Its quite a surprise that its 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 0 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 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 receptormediated off-target liabilities, e.g. hERG ii. Our computer learning methods will greatly improve, to allow us to build good empirical models iii. Well get much better at building empirical 3D models iv. Well have at least pharmacophore models of each receptor-mediated off-target liability v. Well be able to find the data we need. 1 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007

In summary, these are my outlooks for the next 20 years in CADD (contd) 3. Well conquer challenging new classes of drug targets, e.g. PPIs 4. Well learn to design self-assembling drugs 5. Well 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. 2 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 My penultimate comment is to echo Peter Goodford 3 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007 And, finally Thanks, Yvonne, for introducing me to such an endlessly fascinating line of work.

4 / 24 CADD: the next twenty years / J. H. Van Drie / Mar 25, 2007

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