Pharmaceutical Informatics and Computer-AidedDrug Discovery

Sangtae KimExecutive Director, Morgridge Institute for Research

CDS&E Distinguished Seminar Series at Rutgers – October, 10, 2011

Twin institutes under one roof on the UW-Madison campus

Vision Inspired by Wisconsin IdeaStrengthen Wisconsin as world class center for research and

commercialization to improve economy and lives of citizens.

CollaborationSpark research collaborations across the sciences that accelerate breakthrough discoveries to improve human health

InteractionFoster interaction between public and private research that breaks downbarriers between researchers, labs &scientific disciplines

CommunityDevelop vibrant public space on campus that builds community and engages the public in the sciences and humanities

At center of campus science sites

IP PortfoliosUniversity

HealthcareDelivery

IP PortfoliosUniversity

HealthcareDelivery

OutlineThe Priorities for the Pharma-Informatics Department

• Create an information highway from bio- discovery to delivery, from the promise of genomics to the fruits of personalized medicine (population segmentation).

• Systems critique of the R&D Pipeline.• Focus research resources on new and

better methods at the bottlenecks in the discovery and development of new drugs, e.g., lead optimization.

Why pharmaceutical informatics?

Value(log scale)

$

$1 per mg.

$100 per kg.

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Pharmaceutical Informatics

Phase II clinical trials

Informatics’ new frontier

Pharma/Biotech R&D Timeline

CDS&E: Enabling Role of Datain Computer-Aided Drug Design

• Evolution of two distinct branches of computational biology• Molecule wriggling (solving differential equations of

biochemical physics)• Data miners (informatics)

• New generation trained to do both• Limitations of each branch• Example:

Protein Kinases: Major Targets of21st century

Constituents of cell signaling pathwaysPhosphorylation of other proteins• Cancer, Inflammation, Diabetes, …• e.g. MAPK, CDK2, EGFR, PKA, etc.

Largest enzyme family in the genome: 518 members with 7 sub-families.

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Big Pharma’s Kinase Interaction Map • High throughput assay, M. Fabian et al. (Ambit Biosciences)• 113 kinases & 17 kinase inhibitors

approved drugs, candidates in clinical trials, research compounds.

Fabian, M.A., et al., A small molecule-kinase interaction map for clinical kinase inhibitors. Nat. Biotech. 2005, 23(3): p. 329-336.

= Gleevec™(Novartis); Iressa™(AstraZeneca); Tarceva™(Roche); Sutent™(Pfizer); Arxxant™(Lilly); … plus more

Protein Kinase Inhibitors: Selectivity Kinases are cross reactive because of structure, fold conservation.Inhibitory impact across sub-families!

Gleevec®, a Cancer drug, also effective against Diabetes !!Targeted against ABL kinase but inhibits PDGF also.

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Some inhibitors (poisons) bind through non-conservedfeatures.

Pattern is not aligned with evolution and thus not a low hanging fruit for simpler informatics tools.

Protein Kinase Inhibitors: Selectivity Kinases are cross reactive because of structure, fold conservation.Inhibitory impact across sub-families!

Recent (2006) advance in aligning the pattern of reactivity across sub-families:A. Fernandez & S. Maddipati, J. Med. Chem.

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Partially wrapped hydrogen bonds (dehydrons) attract hydrophobic groups to get completely wrapped by the dehydronic force

1

0

H( )G d

=

gromacs simulation package NVT Ensemble

TIP3p water modelPME electrostatics

Nose Hoover thermostat100 equilibrium runs

Computation details:

Packing differences vs. Pharmacological differences

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ExperimentsFabian et al.

Nat. Biotech 2005

TheoryFernandez & Maddipati

J. Med. Chem. 2006

Previous Example: in principle, a hand-off

“results”“hand-off”

Simulation runs

Database of results

Implication: progress via collaboration

When Hand-Offs are Not Possible

“results”

Implication: education and training

Simulation runs

Informatics on the characteristics of the entire run

Dehydrons & Wrapperones™ in Pharmaceutical Informatics

Gleevec™/imatinib on theCover of Time Magazine 2001

High-Throughput-Computing improves anti-cancer drugs• Change research paradigm from “generating lead generation”

to “optimizing lead optimization”!• 1st generation drug candidates (tweaks)• 2nd generation drug candidates (wrapperones™)Success factors enabled by collaboratory environmentDistinguished Investigator: Ariel Fernandez (Aug. 2011)

Re-designing better, next generation anti-cancer drugs:selective wrapping deduced from dehydronic patterns.

A. Fernandez at entry to H.F. DeLuca ForumPhoto taken Feb. 2011 (seminar visit)

Machine learning expert S. Maddipati (right)co-advised by S. Kim and A. Fernandez.

Also shown: R. Nandigam now at Aspen TechPhoto taken summer 2007

“results”

Ultimate: enable sharing of sensitive data

The Future

Societal / Regulatory factors

Closing Thoughts

1925 – Harry SteenbockVitamin D by Irradiation