ABSTRACT. There is a growing need for highly accurate in silico and in vitro predictive models to facilitate drug discovery and development. Results from in vitro permeation studies across the Caco-2 cell monolayer are commonly used for drug permeability screening in industry and are also accepted as a surrogate for human intestinal permeability measurements by the US FDA to support new drug applications. Countless studies carried out in this cell line with published permeability measurements have enabled the development of many in silico prediction models. We identify several common cases that illustrate how using Caco-2 permeability measurements in these in silico and in vitro predictive models will not correlate with human intestinal permeability and will further lead to inaccuracies in these models. We provide guidelines and recommendations for improving these models to more accurately predict clinically relevant information, thereby enhancing the drug discovery, development, and regulatory approval processes.
In modeling blood–brain barrier (BBB) passage, in silico models have yielded ~80% prediction accuracy, and are currently used in early drug discovery. Being derived from molecular structural information only, these models do not take into account the biological factors responsible for the in vivo outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS to improve BBB predictions of oral drugs. BDDCS class membership was integrated with in vitro Pgp efflux and in silico permeability data to create a simple 3-step classification tree that accurately predicted CNS disposition for more than 90% of 153 drugs in our data set. About 98% of BDDCS class 1 drugs were found to markedly distribute throughout the brain; this includes a number of BDDCS class 1 drugs shown to be Pgp substrates. This new perspective provides a further interpretation of how Pgp influences the sedative effects of H1-histamine receptor antagonists.
The biopharmaceutics classification
system (BCS) and biopharmaceutics
drug distribution classification system (BDDCS) are complementary
classification systems that can improve, simplify, and accelerate
drug discovery, development, and regulatory processes. Drug permeability
has been widely accepted as a screening tool for determining intestinal
absorption via the BCS during the drug development and regulatory
approval processes. Currently, predicting clinically significant drug
interactions during drug development is a known challenge for industry
and regulatory agencies. The BDDCS, a modification of BCS that utilizes
drug metabolism instead of intestinal permeability, predicts drug
disposition and potential drug–drug interactions in the intestine,
the liver, and most recently the brain. Although correlations between
BCS and BDDCS have been observed with drug permeability rates, discrepancies
have been noted in drug classifications between the two systems utilizing
different permeability models, which are accepted as surrogate models
for demonstrating human intestinal permeability by the FDA. Here,
we recommend the most applicable permeability models for improving
the prediction of BCS and BDDCS classifications. We demonstrate that
the passive transcellular permeability rate, characterized by means
of permeability models that are deficient in transporter expression
and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately
predict BDDCS metabolism. These systems will inaccurately predict
BCS classifications for drugs that particularly are substrates of
highly expressed intestinal transporters. Moreover, in this latter
case, a system more representative of complete human intestinal permeability
is needed to accurately predict BCS absorption.
Although FDA approved BCS Class 1 drugs are designated as high permeability, in fact, the criterion utilized is high extent of absorption. This ambiguity should be eliminated and the FDA criterion should explicitly be stated as ≥ 90% absorption based on absolute bioavailability or mass balance. Maintaining confidentiality of the drugs for which the FDA has approved BCS waivers of in vivo bioequivalence studies is not good public policy and should be reversed.
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