Elizabeth Joshi

Development of quantitative systems pharmacology and toxicology models within consortia: experiences and lessons learned through DILIsym development

Brett A. Howell, Scott Q Siler, Hugh A. Barton, Paul Brent Watkins et autres

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Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule

Robert P. Sheridan, J. Chris Culberson, Elizabeth Joshi, Matthew Tudor et autres

As with many other institutions, our company maintains many quantitative structure-activity relationship (QSAR) models of absorption, distribution, metabolism, excretion, and toxicity (ADMET) end points and updates the models regularly. We recently examined version-to-version predictivity for these models over a period of 10 …

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Repression of Organic Anion Transporting Polypeptide (OATP) 1B Expression and Increase of Plasma Coproporphyrin Level as Evidence for OATP1B Downregulation in Cynomolgus Monkeys Treated with Chenodeoxycholic Acid

Yueping Zhang, Shen-Jue Chen, Cliff Chen, Xueqing Chen et autres

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Improvement in ADMET Prediction with Multitask Deep Featurization

Evan N. Feinberg, Elizabeth Joshi, Vijay S. Pande, Alan C. Cheng

The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computational chemistry and medicinal chemistry communities in recent decades. Traditional …

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Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure–Activity Relationship Models?

Robert P. Sheridan, Prabha Karnachi, Matthew Tudor, Yuting Xu et autres

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the …

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Strategic Incorporation of Polarity in Heme-Displacing Inhibitors of Indoleamine-2,3-dioxygenase-1 (IDO1)

Catherine A. White, Meredeth A. McGowan, Hua Zhou, Nunzio Sciammetta et autres

Indoleamine-2,3-dioxygenase-1 (IDO1) has emerged as a target of significant interest to the field of cancer immunotherapy, as the upregulation of IDO1 in certain cancers has been linked to host immune evasion and poor prognosis for patients. In particular, IDO1 inhibition is of …

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Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization

Evan N. Feinberg, Robert P. Sheridan, Elizabeth Joshi, Vijay S. Pande et autres

The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) properties of drug candidates are estimated to account for up to 50% of all clinical trial failures. Predicting ADMET properties has therefore been of great interest to the cheminformatics and medicinal chemistry communities in …

Augmenting decision making in early discovery – Efforts to derisk bsep inhibition potential utilizing in silico modeling

Elizabeth Joshi, Michael J. Hafey, Robert Houle, Prabha Karnachi

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Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles

John M. Sanders, Douglas C. Beshore, Joseph C. Culberson, James I. Fells et autres

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical …

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The design and synthesis of novel spirocyclic heterocyclic sulfone ROMK inhibitors as diuretics

Harry R. Chobanian, Yan Guo, Barbara A. Pio, Haifeng Tang et autres

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Synthesis and preliminary PET imaging studies of [18F]MK-6240: A radiotracer for imaging neurofibrillary tangles (NFTs) in Alzheimer's disease

Abbas M. Walji, Idriss Bennacef, Harold G. Selnick, Jing Li et autres

Preclinical Characterization of 18 F-MK-6240, a Promising PET Tracer for In Vivo Quantification of Human Neurofibrillary Tangles

Eric D. Hostetler, Abbas M. Walji, Zhizhen Zeng, Patricia J. Miller et autres

A PET tracer is desired to help guide the discovery and development of disease-modifying therapeutics for neurodegenerative diseases characterized by neurofibrillary tangles (NFTs), the predominant tau pathology in Alzheimer disease (AD). We describe the preclinical characterization of the NFT PET tracer 18F-MK-6240. …

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