Literature DB >> 32164508

Further Considerations Towards an Effective and Efficient Oncology Drug Discovery DMPK Strategy.

Beth Williamson1, Nicola Colclough1, Adrian John Fretland2, Barry Christopher Jones1, Rhys Dafydd Owen Jones1, Dermot Francis McGinnity1.   

Abstract

BACKGROUND: DMPK data and knowledge are critical in maximising the probability of developing successful drugs via the application of in silico, in vitro and in vivo approaches in drug discovery.
METHODS: The evaluation, optimisation and prediction of human pharmacokinetics is now a mainstay within drug discovery. These elements are at the heart of the 'right tissue' component of AstraZeneca's '5Rs framework' which, since its adoption, has resulted in increased success of Phase III clinical trials. With the plethora of DMPK related assays and models available, there is a need to continually refine and improve the effectiveness and efficiency of approaches best to facilitate the progression of quality compounds for human clinical testing.
RESULTS: This article builds on previously published strategies from our laboratories, highlighting recent discoveries and successes, that brings our AstraZeneca Oncology DMPK strategy up to date. We review the core aspects of DMPK in Oncology drug discovery and highlight data recently generated in our laboratories that have influenced our screening cascade and experimental design. We present data and our experiences of employing cassette animal PK, as well as re-evaluating in vitro assay design for metabolic stability assessments and expanding our use of freshly excised animal and human tissue to best inform first time in human dosing and dose escalation studies.
CONCLUSION: Application of our updated drug-drug interaction and central nervous system drug exposure strategies are exemplified, as is the impact of physiologically based pharmacokinetic and pharmacokinetic-pharmacodynamic modelling for human predictions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

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Keywords:  ADME; CNS exposure; Drug discovery; bioavailability; clearance; drug metabolism; pharmacokinetics; volume of distribution.

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Year:  2020        PMID: 32164508     DOI: 10.2174/1389200221666200312104837

Source DB:  PubMed          Journal:  Curr Drug Metab        ISSN: 1389-2002            Impact factor:   3.731


  1 in total

1.  Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Authors:  Andrés Martínez Mora; Vigneshwari Subramanian; Filip Miljković
Journal:  J Comput Aided Mol Des       Date:  2022-05-27       Impact factor: 4.179

  1 in total

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