High throughput physicochemical profiling for drug discovery

Kerns, Edward H.

doi:10.1002/jps.1134

Cited by 326 publications

(207 citation statements)

References 65 publications

(77 reference statements)

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“…The permeability of the 32 compounds was determined using two in vitro permeability assays: parallel artificial membrane permeation assay (PAMPA), and apicalto-basolateral (AϾB) apparent permeability (P app ) in MDCK cells. PAMPA is a model specifically designed to measure passive membrane permeability, which is energy-independent (Kansy et al, 1998;Kerns, 2001;Sugano et al, 2001;Wohnsland and Faller, 2001;Avdeef and Testa, 2002). Transport determined using MDCK cell monolayers approximates passive biological permeability, as the MDCK cells typically show low endogenous transporter activity.…”

mentioning

confidence: 99%

In Vitro P-glycoprotein Assays to Predict the in Vivo Interactions of P-glycoprotein with Drugs in the Central Nervous System

Feng

Mills²,

Davidson³

et al. 2007

Drug Metab Dispos

402

458

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ABSTRACT:Thirty-one structurally diverse marketed central nervous system (CNS)-active drugs, one active metabolite, and seven non-CNSactive compounds were tested in three P-glycoprotein (P-gp) in vitro assays: transwell assays using MDCK, human MDR1-MDCK, and mouse Mdr1a-MDCK cells, ATPase, and calcein AM inhibition. Additionally, the permeability for these compounds was measured in two in vitro models: parallel artificial membrane permeation assay and apical-to-basolateral apparent permeability in MDCK. The exposure of the same set of compounds in brain and plasma was measured in P-gp knockout (KO) and wild-type (WT) mice after subcutaneous administration. One drug and its metabolite, risperidone and 9-hydroxyrisperidone, of the 32 CNS compounds, and 6 of the 7 non-CNS drugs were determined to have positive efflux using ratio of ratios in MDR1-MDCK versus MDCK transwell assays. Data from transwell studies correlated well with the brainto-plasma area under the curve ratios between P-gp KO and WT mice for the 32 CNS compounds. In addition, 3300 Pfizer compounds were tested in MDR1-MDCK and Mdr1a-MDCK transwell assays, with a good correlation (R 2 ‫؍‬ 0.92) between the efflux ratios in human MDR1-MDCK and mouse Mdr1a-MDCK cells. Permeability data showed that the majority of the 32 CNS compounds have moderate to high passive permeability. This work has demonstrated that in vitro transporter assays help in understanding the role of P-gp-mediated efflux activity in determining the disposition of CNS drugs in vivo, and the transwell assay is a valuable in vitro assay to evaluate human P-gp interaction with compounds for assessing brain penetration of new chemical entities to treat CNS disorders.Human P-glycoprotein (P-gp, MDR1) is known to be a determinant of drug absorption, distribution, and excretion of a number of clinically important drugs (Ambudkar et al., 1999;Fromm, 2000). P-gp is widely expressed in major organs, and, more specifically, P-gp is highly expressed in the capillaries of the blood brain barrier (BBB) and poses a barrier to brain penetration of its substrates (Schinkel, 1999). Given that P-gp efflux liability can be a major hurdle for CNS therapeutic drugs to cross the BBB and reach the target, the interactions of CNS compounds with P-gp may lead to the lack of CNS activity as a result of the decreased brain penetration. Thus, the prediction and understanding of the relevance of P-gp-mediated efflux transport have become important activities in the discovery and development of CNS drugs. In attempts to predict the effects of P-gp in vivo, a variety of in vitro P-gp assays have been developed to classify compounds as P-gp substrates. For instance, transwell-based assays using polarized cell lines such as the Madin-Darby canine kidney (MDCK) cell line. The MDCK cell line can be stably transfected with human MDR1 or mouse Mdr1a (MDR1-MDCK or Mdr1a-MDCK, respectively). Comparison of the efflux ratios between MDR1-MDCK and MDCK transwell assays can provide a measure of the specific human P-gp-mediated e...

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mentioning

confidence: 99%

In Vitro P-glycoprotein Assays to Predict the in Vivo Interactions of P-glycoprotein with Drugs in the Central Nervous System

Feng

Mills²,

Davidson³

et al. 2007

Drug Metab Dispos

402

458

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“…The Clean Energy Project stands out from other computational materials science approaches as it combines conventional modeling with strategies from modern drug discovery [54][55][56][57][58][59][60][61]: CEP features an automated, highthroughput infrastructure for a systematic screening of millions of OPV candidates at a first-principles electronic structure level [62]. It also adopts techniques from cheminformatics [63,64] and heavily relies on data mining [65,66].…”

Section: The Harvard Clean Energy Projectmentioning

confidence: 99%

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

Hachmann

Olivares‐Amaya

Atahan-Evrenk

et al. 2011

J. Phys. Chem. Lett.

597

565

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This perspective article introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments.CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource which is provided by distributed volunteer computing on IBM's World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure-property relations in the domain of organic electronics. Such insights can open the door to a rational and systematic design of future high-performance materials. The computational work in CEP is tightly embedded in a collaboration with experimentalists, who provide valuable input and feedback to the project.

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“…Papers on parameter estimation methods have also been selected, e.g. redox potential of cytochrome C [105], apparent dissociation constants [116], overlapping acidity constants [134,149], physicochemical parameters [113,119,141], neutralization enthalpies [148], position and confidence limits of an extreme [155,160], counting measurements [159], linear and non linear calibration response [133], and multiparameter models [153]. The field of prediction is also an important area, e.g.…”

Section: Applicationsmentioning

confidence: 99%

Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications

Martín¹,

Navas²,

Fernandez-Recamales³

et al. 2017

International Journal of Advanced Research in Chemical Science

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High throughput physicochemical profiling for drug discovery

Cited by 326 publications

References 65 publications

In Vitro P-glycoprotein Assays to Predict the in Vivo Interactions of P-glycoprotein with Drugs in the Central Nervous System

In Vitro P-glycoprotein Assays to Predict the in Vivo Interactions of P-glycoprotein with Drugs in the Central Nervous System

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications

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