Passive oral drug absorption can be predicted more reliably by experimental than computational models—Fact or myth

Linnankoski, Johanna; Ranta, Veli‐Pekka; Urtti, Arto

doi:10.1016/j.ejps.2008.03.001

Cited by 26 publications

(15 citation statements)

References 22 publications

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“…Previously, our laboratory has developed QSPKR models to predict biliary clearance and percent of administered dose excreted unchanged into bile in rats and humans (Yang et al, 2009). QSPKR models for the prediction of passive oral absorption have been deemed superior to labor-intensive in vitro and ex vivo permeability assays (Linnankoski et al, 2008). Applications of empirical and mechanism-based QSPKR models for the prediction of various pharmacokinetic processes have been reviewed extensively (Xu and Mager, 2011).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

Dave

Morris

2014

Drug Metab Dispos

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Renal clearance (CL R ), a major route of elimination for many drugs and drug metabolites, represents the net result of glomerular filtration, active secretion and reabsorption, and passive reabsorption. The aim of this study was to develop quantitative structurepharmacokinetic relationships (QSPKR) to predict CL R of drugs or drug-like compounds in humans. Human CL R data for 382 compounds were obtained from the literature.Step-wise multiple linear regression was used to construct QSPKR models for training sets and their predictive performance was evaluated using internal validation (leave-one-out method). All qualified models were validated externally using test sets. QSPKR models were also constructed for compounds in accordance with their 1) net elimination pathways (net secretion, extensive net secretion, net reabsorption, and extensive net reabsorption), 2) net elimination clearances (net secretion clearance, CL SEC ; or net reabsorption clearance, CL REAB ), 3) ion status, and 4) substrate/inhibitor specificity for renal transporters. We were able to predict 1) CL REAB Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics Drug Disposition Classification System classes 1 and 2. In conclusion, constructed parsimonious QSPKR models can be used to predict CL R of compounds that 1) undergo net reabsorption/extensive net reabsorption and 2) are substrates and/or inhibitors of human renal transporters.

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Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

Dave

Morris

2014

Drug Metab Dispos

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“…The bioavailability filter considers the five factors related to druggability and also includes the polar surface area (Յ200 is favorable), defined as the sum of all the surface contributions of polar fragments, and the number of fused aromatic rings (Յ5 is favorable) of the compound (42,43). This filter was shown previously to correlate with passive compound transport through membranes (44). For a compound to be predicted to pass this filter, it must fulfill the requirements for 6 of the 7 properties examined (32).…”

Section: Resultsmentioning

confidence: 99%

Development of anEx VivoLymph Node Explant Model for Identification of Novel Molecules Active against Leishmania major

Peniche

Osorio

Renslo

et al. 2014

Antimicrob Agents Chemother

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e Leishmaniasis is a vector-borne zoonotic infection affecting people in tropical and subtropical regions of the world. Current treatments for cutaneous leishmaniasis are difficult to administer, toxic, expensive, and limited in effectiveness and availability. Here we describe the development and application of a medium-throughput screening approach to identify new drug candidates for cutaneous leishmaniasis using an ex vivo lymph node explant culture (ELEC) derived from the draining lymph nodes of Leishmania major-infected mice. The ELEC supported intracellular amastigote proliferation and contained lymph node cell populations (and their secreted products) that enabled the testing of compounds within a system that mimicked the immunopathological environment of the infected host, which is known to profoundly influence parasite replication, killing, and drug efficacy. The activity of known antileishmanial drugs in the ELEC system was similar to the activity measured in peritoneal macrophages infected in vitro with L. major. Using the ELEC system, we screened a collection of 334 compounds, some of which we had demonstrated previously to be active against L. donovani, and identified 119 hits, 85% of which were confirmed to be active by determination of the 50% effective concentration (EC 50 ). We found 24 compounds (7%) that had an in vitro therapeutic index (IVTI; 50% cytotoxic/effective concentration [CC 50 ]/EC 50 ) > 100; 19 of the compounds had an EC 50 below 1 M. According to PubChem searchs, 17 of those compounds had not previously been reported to be active against Leishmania. We expect that this novel method will help to accelerate discovery of new drug candidates for treatment of cutaneous leishmaniasis.

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“…The ultimate goal of the in silico models of ADME properties is the accurate prediction of the in vivo pharmacokinetic of a potential drug molecule in man, whilst it exists only as a virtual structure (2). The unexpectedly good prediction power of the simple computational models with high-throughput renders them vital tools in the early screening of drug candidates, whereas laborious cell culture models and animal studies can be beneficial in the later phases when comprehensive information about the transport mechanisms is needed (16). There is gradually emerging consensus that in silico predictions are no less predictive to what occurs in vivo than are in vitro tests, with the distinct advantage that far less investment in technology, resources, and time is needed (17).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pharmacokinetic Parameters

Мадан

Dureja²

2012

Methods in Molecular Biology

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In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

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Passive oral drug absorption can be predicted more reliably by experimental than computational models—Fact or myth

Cited by 26 publications

References 22 publications

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

Development of anEx VivoLymph Node Explant Model for Identification of Novel Molecules Active against Leishmania major

Prediction of Pharmacokinetic Parameters

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