Biopharmaceutics Drug Disposition Classification System Research Articles

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system. In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models. The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy. Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

Vital Medications in the World According to Preliminary BCS Classification: Prognostication of Solubility and Permeability

The Biopharmaceutical Classification System (BCS) has been used as a predictive method to evaluate potential formulation impacts on oral medication bioavailability in humans. The BCS can assist in the prediction of in vivo product performance and the construction of mechanistic models that enable formulation assessments through the generation of scenarios when used in conjunction with in vitro dissolution testing. The applicability of current human BCS standards has not yet been examined in dogs, which restricts its application in canine medication development. The BCS has had a significant impact on drug discovery, development, and regulation on a global scale and the BCS continues to receive much validation, discussion, and extension in the literature. Drug regulatory organizations from all across the world have fully deployed the BCS when establishing bioavailability/bio-equivalence requirements for the approval of oral drugs with instant release (IR). In this study, we examine the provisional BCS classification of the most popular medications sold worldwide and discuss the BCS scientific framework and its influence on regulatory exercise of oral medicinal products. Also mentioned is the Biopharmaceutical Drug Disposition Classification System and how it relates to the BCS.

State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References

The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0–6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS) versus BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.Graphical abstract

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

Accurate prediction of in vivo hepatic clearance is an essential part of successful and efficient drug development; however, many investigators have recognized that there are significant limitations in the predictability of clearance with a tendency for underprediction for primarily metabolized drugs. Here, we examine the impact of adding serum or albumin into hepatocyte and microsomal incubations on the predictability of in vivo hepatic clearance. The addition of protein into hepatocyte incubations has been reported to improve the predictability for high clearance (extraction ratio) drugs and highly protein-bound drugs. Analyzing published data for 60 different drugs and 97 experimental comparisons (with 17 drugs being investigated from two to seven) we confirmed the marked underprediction of clearance. However, we could not validate any relevant improved predictability within twofold by the addition of serum to hepatocyte incubations or albumin to microsomal incubations. This was the case when investigating all measurements, or when subdividing analyses by extraction ratio, degree of protein binding, Biopharmaceutics Drug Disposition Classification System class, examining Extended Clearance Classification System class 1B drugs only, or drug charge. Manipulating characteristics of small data sets of like compounds and adding scaling factors can appear to yield good predictability, but the carryover of these methods to alternate drug classes and different laboratories is not evident. Improvement in predictability of poorly soluble compounds is greater than that for soluble compounds, but not to a meaningful extent. Overall, we cannot confirm that protein addition improves in vitro-in vivo extrapolation predictability to any clinically meaningful degree when considering all drugs and different subsets. SIGNIFICANCE STATEMENT: The addition of protein into microsomal or hepatocyte incubations has been widely proposed to improve hepatic clearance predictions. To date, studies examining this phenomenon have not included appropriate negative controls where predictability is achieved without protein addition and have been conducted with small data sets of similar compounds that don't apply to alternate drug classes. Here, an extensive analysis of published data for 60 drugs and 97 experimental comparisons couldn't validate any relevant clinically improved clearance predictability with protein addition.

Untangling Absorption Mechanisms and Variability in Bioequivalence Studies Using Population Analysis.

Both inter-individual (IIV) and inter-occasion (IOV) variabilities are observed in bioequivalence studies. High IOV may be a cause of problems on the demonstration of bioequivalence, despite strict measures are taken to control it. The objective of this study is to investigate further means of controlling IIV by optimizing study design of crossover studies. Data from 18 bioequivalence studies were used to develop population pharmacokinetics (popPK) models to characterize the absorption and disposition processes of 14 drugs, to estimate IOV for each drug substance and to evaluate possible correlations with biopharmaceutical properties of drug substances, classified in accordance to the Biopharmaceutics Drug Disposition Classification System (BDDCS). Plasma-pharmacokinetics profiles for the 14 drugs analyzed were successfully described using popPK. The pharmacokinetic parameters that showed greater variability were first-order rate constant of absorption, duration of the zero-order absorption process, relative bioavailability and time of latency. ISCV% estimated for Cmax seems to correlate with the log-Dose-Number for Class 1, 2 and 3, despite no direct correlation was observed between popPK model residual variability (RUV) and ISCV%. Nevertheless, higher RUV estimates were observed for Class 2 drugs in comparison to Class 1 and 3. Pharmacokinetic parameters related to drug absorption showed greater variability. Ingestion of the IMP along with 240 mL of water showed to standardize gastric emptying. Given the dependency between Cmax variability and dose-solubility ratio, for classes 2 and 4, ad libitum water intake may increase Cmax and AUC ISCV%. A water ingestion standardization until the expected Tmax of the drug is suggested.

Preparation of Aspirin Nanosuspension by Antisolvent Precipitation Method

This study aims to enhance the dissolution rate of a poorly-soluble drug, aspirin, by fabricating aspirin nanosuspensions using the anti-solvent precipitation. The  study investigates the effect of the type of solvents, solvent to anti-solvent ratio, drug concentration, machines, stirring speed, ultrasonication technique and the temperature of solvent on the particle size and polydispersity index. The characterization of the original aspirin powder and nanoparticles was evaluated by differential scanning calorimetry and in vitro dissolution test. The results indicate that the selected formulation showed the smallest mean size of 228.2 ± 24.6 nm and a zeta potential of - 40.3 ± 2.5 mV. The differential scanning calorimetry analysis demonstrates that aspirin nanoparticles possessed lower crystallinity than the raw aspirin powder. The dissolution of nanoparticle was significantly higher compared with the original drug in the in vitro dissolution test.
 Keywords: Aspirin, nanosuspension, anti-solvent precipitation, differential scanning calorimetry, dissolution.
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Effects of Single Dose Rifampin on the Pharmacokinetics of Fluvastatin in Healthy Volunteers.

The objective of this study was to determine the effects of the OATP inhibitor rifampin on pharmacokinetic of Biopharmaceutics Drug Disposition Classification System Class 1 compound fluvastatin. A crossover study was carried out in 10 healthy subjects who were randomized to 2 phases to receive fluvastatin 20mg orally alone and following a 30-minute 600mg i.v. infusion of rifampin. The results demonstrated that i.v. rifampin increased the mean area under the plasma fluvastatin concentration-time curve (AUC0-∞ ) by 255%, mean peak plasma concentration (Cmax ) by 254%, decreased oral volume of distribution by 71%, whereas the mean elimination terminal half-life (T1/2 ), mean absorption time (MAT), and time to peak concentration (Tpeak ) of fluvastatin did not significantly change. The study demonstrated that rifampin exhibited a significant drug interaction with fluvastatin. The mechanism of the increased plasma concentrations is likely due to inhibition of OATP transporters in hepatocytes.

Effect of carrier type and Tween® 80 concentration on the release of silymarin from amorphous solid dispersions

Silymarin is a mixture of flavonolignans obtained from the seeds of milk thistle (Silybum marianum L. Gaertner). Silymarin behaves as a weak acid and is categorised as a class IV drug substance in accordance with biopharmaceutics drug disposition classification system, possessing low solubility, as well as low bioavailability. The scope of this study was to identify possible formulation strategies of silymarin. Then, the main aim was to manufacture silymarin solid dispersions using a solvent evaporation approach and to characterise the physicochemical and drug release properties of the two formulations containing two different porous carriers, namely Avicel® PH-102 and Syloid® XDP 3150, and different concentrations of Tween® 80. Silymarin Log P was determined to be 1.6 (±0.14) negating the possibility of bypassing first pass metabolism via lymphatic transport. Utilising alkaline titration, the apparent pKa of silymarin was found to be similar to that of the silybin pKa (5.68). The crystallinity of raw silymarin was confirmed using powder X-ray diffraction and differential scanning calorimetry, and its thermal degradation was observed at a temperature higher than 220 °C (thermogravimetric analysis). Avicel® PH-102 and Syloid® XDP 3150 were characterised in terms of morphology using scanning electron microscopy, particle size distribution (laser diffraction spectroscopy), pore size distribution and intra-particle porosity using mercury intrusion porosimetry. Solid dispersions were manufactured using an organic solvent method incorporating silymarin, the carrier and optionally Tween® 80. The amorphous state of silymarin in all prepared formulations was confirmed using differential scanning calorimetry and powder X-ray diffraction. Silymarin dissolution kinetics were faster for Syloid® XDP 3150 versus Avicel® PH-102 and explained through carrier properties. The addition of Tween® 80 and increasing the concentration from 0.3 to 1.6% (w/w) significantly increased the drug release kinetics of Avicel® PH-102 formulations but had no effect on Syloid® XDP 3150 formulations. Drug release from prepared formulations was compared with Legalon® 70 using the similarity factor (F2). Syloid® XDP 3150-based formulations showed F2>50%. Tween® 80 had a negligible effect on the silymarin release from Syloid® XDP 3150-based formulations. Interestingly, the ability of Tween® 80 to inhibit gut wall efflux is well known. Thus, the inclusion of this excipient offers an opportunity to modulate the silymarin bioavailability without changing the drug release profile. A six-month stability study (at room temperature and 40% RH) confirmed that solid dispersions were still powder X-ray diffraction and differential scanning calorimetry amorphous. Acetone was used for both silymarin extraction and preparation of solid dispersions. Thus, there is an opportunity to use a single step to both load silymarin and form solid dispersions within a single-step.

A perspective on the physicochemical and biopharmaceutic properties of marketed antiseizure drugs-From phenobarbital to cenobamate and beyond.

The success rate from first time in man to regulatory approval of central nervous system (CNS) drugs is lower than the overall success rate across all therapeutic indications (eg, cardiovascular, infectious diseases). To understand the reasons for drug-candidate failure and to capture trends in antiseizure drug (ASD) design, we have analyzed the physicochemical and biopharmaceutical properties of marketed ASDs in comparison with new ASDs in development. Our comparative analysis included molecular weight (MW), logP, polar surface area (PSA), the "Lipinski rule of five," and the CNS Multiparameter Optimization (MPO) score. LogP is the logarithm of a drug-partition coefficient (P) between n-octanol and water. PSA is the molecule's surface sum of its polar atoms. ASDs' biopharmaceutical properties were classified according to their water solubility, permeability, and route of elimination as outlined by the Biopharmaceutics Classification System (BCS) and Biopharmaceutics Drug Disposition Classification System (BDDCS). For old ASDs (1912-1990), logP, PSA, and CNS MPO values ranged between 0.4 and 2.8, 37 and 87Å2 , and 4.4 and 6.0, respectively. For second-generation ASDs (1990-2008), PSA values ranged between 39 and 116Å2 . However, logP values showed a difference between the lipophilic (logP=0.3-3.21) and hydrophilic (logP=-0.6 to -2.16) ASDs. For third-generation ASDs (2008-2020), logP and PSA ranged between 0.3 and 3.5 and between 57 and 76Å2 , respectively. The mean CNS MPO scores of all marketed ASDs were similar, ranging between 4.9 and 5.4, and were similar to those of the ASDs in development (3.5-5.8). Most ASDs belong to BCS and BDDCS classes 1 and 2. MW, logP, CNS MPO score, and PSA assess lipophilicity and correlate with antiseizure activity. To succeed, a new small-molecule ASD must have MW<375 and PSA<140Å2 , belong to BCS and/or BDDCS class 1 or 2, and obey the Lipinski rule of five: logP <5, MW<500, and <5 and <10 of hydrogen-bond donors and acceptors, respectively. The similarity in the MW, logP, and PSA values of marketed and new drugs in development indicates a conservative trend in ASD design.

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Bayesian machine learning models with our in-house software, Assay Central. The performance of all machine learning models was assessed through both the internal 5-fold cross-validation metrics and prediction accuracy of an external test set of compounds with known hepatotoxicity. The best-performing Bayesian model was based on the DILI-concern category from the DILIRank database with an ROC of 0.814, a sensitivity of 0.741, a specificity of 0.755, and an accuracy of 0.746. A comparison of alternative machine learning algorithms, such as k-nearest neighbors, support vector classification, AdaBoosted decision trees, and deep learning methods, produced similar statistics to those generated with the Bayesian algorithm in Assay Central. This study demonstrates machine learning models grouped in a tool called MegaTox that can be used to predict early-stage clinical compounds, as well as recent FDA-approved drugs, to identify potential DILI.

Predicting Interactions between Rifampin and Antihypertensive Drugs Using the Biopharmaceutics Drug Disposition Classification System.

Lack of blood pressure control is often seen in hypertensive patients concomitantly taking antituberculosis medications due to the complex drug-drug interactions between rifampin and antihypertensive drugs. Therefore, it is of clinical importance to understand the underlying mechanisms of these interactions to help formulate recommendations on the use of antihypertensive drugs in patients taking these medications concomitantly. Our objective was to assess the reliability of the Biopharmaceutics Drug Disposition Classification System (BDDCS) to predict potential interactions between rifampin and antihypertensive drugs and thus provide recommendations on the choice of antihypertensive drugs in patients receiving rifampin. Evidence-based in vitro and in vivo predictions of drug-drug interactions. We systematically evaluated interactions between rifampin and antihypertensive drugs using the theory of the BDDCS, taking into consideration the role of drug transporters and metabolic enzymes involved in these interactions. We provide recommendations on the selection of antihypertensive drugs for patients with tuberculosis. Antihypertensive drugs approved by the U.S. Food and Drug Administration and the China National Medical Products Administration were included in this study. The drugs were classified into four categories under the BDDCS classification. Detailed information on cytochrome P450 (CYP) enzymes and drug transporters for each antihypertensive drug was searched in PubMed and other electronic databases. This information was combined with the effects of rifampin on CYP enzymes and drug transporters, and the direction and relative extent of the potential interactions between rifampin and antihypertensive drugs were predicted. Recommendations were then made using the theory of BDDCS. A thorough systematic literature review was performed, and data from all published human studies and case reports were summarized for the validation of our predictions. Interventional and observational studies published in PubMed and two Chinese databases (CNKI and WanFang) through December 16, 2019, were included, and data were extracted for validation of the predictions. Using the BDDCS theory, class 3 active drugs were predicted to exhibit minimal interactions with rifampin. On reviewing case reports and pre-post studies, the predictions we made were found to be reliable. When antituberculosis medications that include rifampin are started in patients with hypertension, it is recommended that the use of calcium channel blockers and classes 1 and 2 β-blockers be avoided. Angiotensin-converting enzyme inhibitors, olmesartan, class 3 β-blockers, spironolactone, and hydrochlorothiazide would be preferable because clinically relevant interactions would not be expected. Application of the BDDCS to predict interactions between rifampin and antihypertensive drugs for patients with both tuberculosis and hypertension was found to be reliable. It should be noted, however, that based on the CYP enzyme and drug transporter information we reviewed, the mechanisms of all of the interactions could not be elucidated, and the predictions are only based on theory. The real effects of rifampin on antihypertensive drugs need to be further observed. More studies in both animals and humans are needed in the future.

Reconciling Human-Canine Differences in Oral Bioavailability: Looking beyond the Biopharmaceutics Classification System.

The extrapolation of oral bioavailability (F) information between dogs and humans has had an important role in the drug development process, whether it be to support an assessment of potential human pharmaceutical formulations or to identify the bioavailability challenges that may be encountered in dogs. Accordingly, these interspecies extrapolations could benefit from a tool that helps identify those drug characteristics consistent with species similarities in F. Our initial effort to find such a tool led to an exploration of species differences as it pertained to the biopharmaceutics classification system (BCS). However, using a range of compounds, we concluded that solubility and permeability alone could not explain interspecies inconsistencies in estimates of F. Therefore, we have now extended our evaluation to include canine versus human comparisons of F based upon the biopharmaceutics drug disposition classification system (BDDCS) and the extended clearance classification system (ECCS). Using the same data as that in our initial BCS assessments, we conclude that although neither the BDDCS nor the ECCS can reliably improve our ability to determine when F will be similar in humans and dogs, the ECCS provides a mechanism to help define possible causes for observed human-canine inconsistencies.

Prediction of Biopharmaceutical Drug Disposition Classification System (BDDCS) by Structural Parameters.

Modeling of physicochemical and pharmacokinetic properties is important for the prediction and mechanism characterization in drug discovery and development. Biopharmaceutics Drug Disposition Classification System (BDDCS) is a four-class system based on solubility and metabolism. This system is employed to delineate the role of transporters in pharmacokinetics and their interaction with metabolizing enzymes. It further anticipates drug disposition and potential drug-drug interactions in the liver and intestine. According to BDDCS, drugs are classified into four groups in terms of the extent of metabolism and solubility (high and low). In this study, structural parameters of drugs were used to develop classification-based models for the prediction of BDDCS class. Reported BDDCS data of drugs were collected from the literature, and structural descriptors (Abraham solvation parameters and octanol-water partition coefficient (log P)) were calculated by ACD/Labs software. Data were divided into training and test sets. Classification-based models were then used to predict the class of each drug in BDDCS system using structural parameters and the validity of the established models was evaluated by an external test set. The results of this study showed that log P and Abraham solvation parameters are able to predict the class of solubility and metabolism in BDDCS system with good accuracy. Based on the developed methods for prediction solubility and metabolism class, BDDCS could be predicted in the correct with an acceptable accuracy. Structural properties of drugs, i.e. logP and Abraham solvation parameters (polarizability, hydrogen bonding acidity and basicity), are capable of estimating the class of solubility and metabolism with an acceptable accuracy.

Cytotoxic 2‐(2′‐Hydroxyphenyl)benzothiazolylquinoline Analogues and their In Vitro Screening through Developmental Assays†

AbstractXenobiotics good pharmacodynamic effect should be evaluated with acceptable drug like properties during the drug discovery process. Benzothiazolyl‐quinoline derivatives are with reported biological and anticancer activity. In the present study, compounds were screened for potency determination in Caco‐2 (Human Epithelial Colorectal Adenocarcinoma) cell line and the most potent compound was evaluated through some of the drug development assays. Experimental protocols were set for stability and solubility (in buffer pH 7.4), metabolic stability, metabolite profiling, Caco‐2 permeability, P‐gp (P‐glycoprotein) inhibition in MDCK (Madin‐Darbey Canine Kidney)‐MDR1 overexpressed cells and CYP induction study by using LC‐MS/MS(Liquid Chromatography Tandem Mass spectrometry) method. The compound was stable and soluble in buffer pH 7.4, less permeable across Caco‐2 cell monolayer, unstable in microsomes, inhibitor of efflux P‐gp transporter and an inducer CYP (Cytochrome P450) 1 A2. The most potent compound (i. e. 6 k) was with high solubility, low permeability and high CLint (intrinsic clearance) in liver microsomes. Therefore it was preliminary classified as class III compound as per biopharmaceutics classification system (BCS) and Class I compound as per biopharmaceutical drug disposition classification system (BDDCS).

Examining P-gp efflux kinetics guided by the BDDCS – Rational selection of in vitro assay designs and mathematical models

The generation of reliable kinetic parameters to describe P-glycoprotein (P-gp) activity is essential for predicting the impact of efflux transport on gastrointestinal drug absorption. The compound-specific selection of in vitro assay designs and ensuing data analysis methods is explored in this manuscript. We measured transcellular permeability and cellular uptake of five P-gp substrates in Caco-2 and LLC-PK1 MDR1 cells. Kinetic parameters of P-gp-mediated efflux transport (Km, Vmax) were derived from conventional and mechanistic compartmental models. The estimated apparent Km values based on medium concentrations in the conventional permeability model indicated significant differences between the cell lines. The respective intrinsic Km values based on unbound intracellular concentrations in the mechanistic compartmental models were significantly lower and comparable between cell lines and assay formats. Non-specific binding or lysosomal trapping were shown to cause discrepancies in the kinetic parameters obtained from different assay formats. A guidance for the selection of in vitro assays and kinetic assessment methods is proposed in line with the Biopharmaceutics Drug Disposition Classification System (BDDCS). The recommendations are expected to aid the acquisition of robust and reproducible kinetic parameters of P-gp-mediated efflux transport.

Intestinal Transporter-Associated Drug Absorption and Toxicity.

Oral drug administration is the most favorable route of drug administration in the clinic. Intestinal transporters have been shown to play a significant role in the rate and extent of drug absorption of some, but not all, drug molecules. Due to the heterogeneous expression of multiple transporters along the intestine, the preferential absorption sites for drugs may vary significantly. In this chapter, we aim to summarize the current research on the expression, localization, function, and regulation of human intestinal transporters implicated in altering the absorption of low to medium molecular weight drug molecules. The role played by bile acid transport proteins (e.g., ASBT and OST-α/β) is included in the discussion. The synergistic action of intestinal drug metabolism and transport is also discussed. Despite the complicated regulatory factors, the biopharmaceutics drug disposition classification system (BDDCS) put forward by Wu and Benet may help us better predict the effect of transporters on drug absorption. The drug-induced toxicity in the intestine, which may result from drug-drug interaction, gut microbiota, and bile salt toxicity, is also discussed.

Evaluation of the Relevance of DILI Predictive Hypotheses in Early Drug Development: Review of In Vitro Methodologies vs BDDCS Classification.

Our published analyses suggest that comparison of proposed DILI prediction methodologies with BDDCS classification is a useful tool to evaluate the potential reliability of newly proposed algorithms, although BDDCS classification itself is not sufficiently predictive. Almost all of the predictive DILI metrics do no better than just avoiding BDDCS Class 2 drugs, although some early data with microliver platforms enabling long-enduring metabolic competency show promising results.

Measures of BSEP Inhibition In Vitro Are Not Useful Predictors of DILI.

Inhibition of the bile salt export pump (BSEP) by a drug has been implicated as a risk factor for a drug's potential to cause drug-induced liver injury (DILI) and is thought to be an important mechanism leading to DILI. For a wide variety of drugs a correlation has been observed between the potency of in vitro BSEP inhibition and its propensity to cause DILI in humans. These findings were interpreted to suggest that BSEP inhibition could be an important mechanism to help explain how some drugs initiate DILI. Because the Biopharmaceutics Drug Disposition Classification System (BDDCS) can be useful in characterizing and predicting some important transporter effects in terms of drug-drug interactions, we evaluated the information provided by BDDCS in order to understand the inhibition propensity of BSEP. Here we analyze the relationship between a compound's ability to inhibit BSEP function and cause liver injury in humans using a compilation of published DILI datasets that have screened for BSEP inhibitors, other hepatic transporters and other mechanism-based toxicity key events. Our results demonstrate that there is little support for in vitro BSEP inhibition being universally DILI predictive. Rather we show that most potent BSEP inhibitors are BDDCS class 2 drugs, which we have demonstrated previously is the BDDCS class most likely to be DILI related. Since BDDCS class is not related to any proposed DILI mechanistic hypotheses, we maintain that if measures of BSEP inhibition alone or together with inhibition of other transporters cannot be differentiated from class 2 assignment, there is no support for in vitro BSEP inhibition being DILI predictive.