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Dr. Sanjivanjit Bhal is a Technical Specialist at Advanced Chemistry Development, Inc. Dr. Bhal obtained her Ph.D. in synthetic organic chemistry at the University of Reading U.K. with Prof. J. Mann, focusing on synthetic elaboration of cyclopentenones for application in solid phase combinatorial chemistry. As part of this research she spent some time at Novartis (Horsham U.K.) working with their combinatorial chemistry/solid phase synthesis group. She completed her post-doctoral fellowship at the Institute of Cancer Research/Imperial Cancer Research Fund (London, U.K.) researching the hot topic of telomerase inhibitors as potential anti-cancer drugs.
After moving to Canada, Dr. Bhal joined Signalgene Inc. (Guelph, ON.) as a medicinal chemist where she further pursued her interest in cancer research, specifically in the field of breast cancer therapeutics. Prior to joining ACD/Labs Dr. Bhal worked for NAEJA Pharmaceuticals (Edmonton, AB.) where she was involved in analog synthesis for lead optimization and early process development chemistry for Pfizer (St. Louis, USA).
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An In-Silico Approach to Reduce the Burdens of Lead Discovery and Optimization
Sanji Bhal and David Adams, Advanced Chemistry Development, Inc. (ACD/Labs)
Lead discovery and optimization are the challenging endeavors of balancing the efficacy of potential drugs with their pharmacokinetic properties. Although potency is essential, so is the ability of a compound to penetrate through various biological barriers and act upon the intended target site.
Use of physicochemical property predictors has long been known to help filter possible hits for desired ADME characteristics. However, the prediction quality is frequently dependent on the particular chemistry used in the training set. ACD/Labs' predictions are based on an additive-constitutive fragmental approach and therefore offer some unique advantages. This approach offers the capability to easily increase the prediction accuracy of specific, often novel, classes of compounds by training the algorithms with experimentally measured pKa, logD, and now solubility data. Moreover, this approach has allowed the development of a unique software tool utilized in lead optimization to rapidly identify structural modifications. These modifications are expected to give analogs with improved physicochemical properties for better bioavailability, absorption through the GI tract, and penetration (or lack of penetration) through the blood brain barrier. In this presentation, we will introduce examples of how this software can be applied to adjust physicochemical properties that directly impact the in-vivo behavior of drugs.
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Using Physicochemical Property Predictions to Overcome ADME Concerns at Lead Optimization
Sanji Bhal and David Adams, Advanced Chemistry Development, Inc. (ACD/Labs)
In this workshop we will be focusing on the application of ACD/Structure Design Suite—a software tool that uses a combination of our physicochemical predictors and a critically evaluated substituent database. With this software, the medicinal chemist can quickly evaluate the biological effect of structural modifications, and design a selection of analogs with enhanced physicochemical properties. This software-aided approach allows the chemist to retain the pharmacaphore and proposes a diverse range of substituents to adjust selected parameters such as solubility, logP or pKa. We will be demonstrating the software and discussing its capabilities, as well as the PhysChem property predictors that are an integral part of this module.
We invite attendees to submit ADME-related lead optimization problems they are experiencing in their laboratory due to physicochemical liabilities, such as low solubility, three weeks before the workshop. Results from the software will be discussed for select examples (please indicate the pharmacaphore to be retained and any other limitations to structural modification).
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