Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions

Design of greener drugs: aligning parameters in pharmaceutical R&D and drivers for environmental impact

Active pharmaceutical ingredients (APIs) in the environment, primarily resulting from patient excretion, are of concern because of potential risks to wildlife. This has led to more restrictive regulatory policies. Here, we discuss the 'benign-by-design' approach, which encourages the development of environmentally friendly APIs that are also safe and efficacious for patients. We explore the challenges and opportunities associated with identifying chemical properties that influence the environmental impact of APIs. Although a straightforward application of greener properties could hinder the development of new drugs, more nuanced approaches could lead to drugs that benefit both patients and the environment. We advocate for an enhanced dialogue between research and development (R&D) and environmental scientists and development of a toolbox to incorporate environmental sustainability in drug development.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Examination of Urinary Excretion of Unchanged Drug in Humans and Preclinical Animal Models: Increasing the Predictability of Poor Metabolism in Humans

PURPOSE

A dataset of fraction excreted unchanged in the urine (fe) values was developed and used to evaluate the ability of preclinical animal species to predict high urinary excretion, and corresponding poor metabolism, in humans.

METHODS

A literature review of fe values in rats, dogs, and monkeys was conducted for all Biopharmaceutics Drug Disposition Classification System (BDDCS) Class 3 and 4 drugs (n=352) and a set of Class 1 and 2 drugs (n=80). The final dataset consisted of 202 total fe values for 135 unique drugs. Human and animal data were compared through correlations, two-fold analysis, and binary classifications of high (fe ≥30%) versus low (<30%) urinary excretion in humans. Receiver Operating Characteristic curves were plotted to optimize animal fe thresholds.

RESULTS

Significant correlations were found between fe values for each animal species and human fe (p<0.05). Sixty-five percent of all fe values were within two-fold of human fe with animals more likely to underpredict human urinary excretion as opposed to overpredict. Dogs were the most reliable predictors of human fe of the three animal species examined with 72% of fe values within two-fold of human fe and the greatest accuracy in predicting human fe ≥30%. ROC determined thresholds of ≥25% in rats, ≥19% in dogs, and ≥10% in monkeys had improved accuracies in predicting human fe of ≥30%.

CONCLUSIONS

Drugs with high urinary excretion in animals are likely to have high urinary excretion in humans. Animal models tend to underpredict the urinary excretion of unchanged drug in humans.

A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SR_MC (r² = 0.81) and SR_LC (r² = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SR_MC r² = 0.74; SR_LC r² = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD_6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs.

Genetic Diversity in Drug Transporters: Impact in African Populations

Polymorphisms in drug transporters, like the adenosine triposphate-binding cassette (ABC) and solute carrier (SLC) superfamilies, may contribute to the observed diversity in drug response in African patients. This review aims to provide a comprehensive summary and analysis of the frequencies and distributions in African populations of ABC and SLC variants that affect drug pharmacokinetics (PK) and pharmacodynamics (PD). Of polymorphisms evaluated in African populations, SLCO1B1 rs4149056 and SLC22A6 rs1158626 were found at markedly higher frequencies than in non-African populations. SLCO1B1 rs4149056 was associated with reduction in rifampin exposure, which has implications for dosing this important anti-tuberculosis therapy. SLC22A6 rs1158626 was associated with increased affinity for antiretroviral drugs. Genetic diversity in SLC and ABC transporters in African populations has implications for conventional therapies, notably in tuberculosis and HIV. More PK and PD data in African populations are needed to assess potential for a different response to drugs compared with other global populations.

Physicochemical and Pharmacokinetic Analysis of Anacardic Acid Derivatives

Anacardic acid (AA) and its derivatives are well-known for their therapeutic applications ranging from antitumor, antibacterial, antioxidant, anticancer, and so forth. However, their poor pharmacokinetic and safety properties create significant hurdles in the formulation of the final drug molecule. As a part of our endeavor to enhance the potential and exploration of the anticancer activities, a detailed study on the properties of selected AA derivatives was performed in this work. A comprehensive analysis of the drug-like properties of 100 naturally occurring AA derivatives was performed, and the results were compared with certain marketed anticancer drugs. The work focused on the understanding of the interplay among eight physicochemical properties. The relationships between the physicochemical properties, absorption, distribution, metabolism, and excretion attributes, and the in silico toxicity profile for the set of AA derivatives were established. The ligand efficacy of the finally scrutinized 17 AA derivatives on the basis of pharmacokinetic properties and toxicity parameters was further subjected to dock against the potential anticancer target cyclin-dependent kinase 2 (PDB ID: 1W98). In the docked complex, the ligand molecules (AA derivatives) selectively bind with the target residues, and a high binding affinity of the ligand molecules was ensured by the full fitness score using the SwissDock Web server. The BOILED-Egg model shows that out of 17 scrutinized molecules, 3 molecules exhibit gastrointestinal absorption capability and 14 molecules exhibit permeability through the blood-brain barrier penetration. The analysis can also provide some useful insights to chemists to modify the existing natural scaffolds in designing new anacardic anticancer drugs. The increased probability of success may lead to the identification of drug-like candidates with favorable safety profiles after further clinical evaluation.

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.

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

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated. A number of experimental approaches to predict human DILI have been proposed utilizing in vitro screening such as inhibition of mitochondrial function, hepatobiliary transporter inhibition, reactive metabolite formation with and without covalent binding, and cellular health, but they have achieved only minimal success. Several studies have shown total administered dose alone or in combination with drug lipophilicity to be correlated with a higher risk of DILI. However, it would be best to have a predictive DILI methodology early in drug development, long before the clinical dose is known. Here we discuss the extent to which Biopharmaceutics Drug Disposition Classification System (BDDCS) defining characteristics, independent of knowing actual drug pharmacokinetics/pharmacodynamics and dose, can be used to evaluate prior published predictive proposals. Our results show that BDDCS Class 2 drugs exhibit the highest DILI severity, and that all of the short-lived published methodologies evaluated here, except when daily dose is known, do not yield markedly better predictions than BDDCS. The assertion that extensively metabolized compounds are at higher risk of developing DILI is confirmed, but can be enhanced by differentiating BDDCS Class 2 from Class 1 drugs.

CONCLUSION

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.

An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library

In vitro-in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (C_max/AC₅₀), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (f_up) and intrinsic hepatic clearance (CL_int) parameters were estimated in silico and incorporated in a three-compartment toxicokinetic (TK) model to first predict C_max for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3925 unique chemicals with curated activity in the HTS data using high-quality dose-response model fits and ≥40% efficacy gave "possible" human in vivo interaction likelihoods lower than median human exposures predicted in the United States Environmental Protection Agency's ExpoCast program. A publicly available web application has been designed to provide all Tox21-ToxCast dose-likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters and can be thought of as an important step toward estimating plausible biological interactions in a high-throughput risk-assessment framework.

Evaluation of DILI Predictive Hypotheses in Early Drug Development

Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals. DILI has been shown to be dependent on both daily dose and extent of hepatic metabolism. Yet, early in drug development daily dose is unknown. Here, we perform a comprehensive analysis of the published hypotheses that attempt to predict DILI, including a new analysis of the Biopharmaceutics Drug Disposition Classification System (BDDCS) in evaluating the severity of DILI warnings in drug labels approved by the FDA and the withdrawal status due to adverse drug reactions (ADRs). Our analysis confirms that higher doses ≥50 mg/day lead to increased DILI potential, but this property alone is not sufficient to predict the DILI potential. We evaluate prior attempts to categorize DILI such as Rule of 2, BSEP inhibition, and measures of key mechanisms of toxicity compared to BDDCS classification. Our results show that BDDCS Class 2 drugs exhibit the highest DILI severity and that all of the published methodologies evaluated here, except when daily dose is known, do not yield markedly better predictions than BDDCS. The assertion that extensive metabolized compounds are at higher risk of developing DILI is confirmed but can be enhanced by differentiating BDDCS Class 2 from Class 1 drugs. We do not propose that the BDDCS classification, which does not require knowledge of the clinical dose, is sufficiently predictive/accurate of DILI potential for new molecular entities but suggest that comparison of proposed DILI prediction methodologies with BDDCS classification is a useful tool to evaluate the potential reliability of newly proposed algorithms.

CONCLUSION

The most successful approaches to predict DILI potential all include a measure of dose, yet there is a quantifiable uncertainty associated with the predicted dose early in drug development. Here, we compare the possibility of predicting DILI potential using the BDDCS classification versus previously published methods and note that many hypothesized predictive DILI metrics do no better than just avoiding BDDCS Class 2 drugs.

Comparison of methods for the prediction of human clearance from hepatocyte intrinsic clearance for a set of reference compounds and an external evaluation set

1. We compared direct scaling, regression model equation and the so-called "Poulin et al." methods to scale clearance (CL) from in vitro intrinsic clearance (CL_int) measured in human hepatocytes using two sets of compounds. One reference set comprised of 20 compounds with known elimination pathways and one external evaluation set based on 17 compounds development in Merck (MS). 2. A 90% prospective confidence interval was calculated using the reference set. This interval was found relevant for the regression equation method. The three outliers identified were justified on the basis of their elimination mechanism. 3. The direct scaling method showed a systematic underestimation of clearance in both the reference and evaluation sets. The "Poulin et al." and the regression equation methods showed no obvious bias in either the reference or evaluation sets. 4. The regression model equation was slightly superior to the "Poulin et al." method in the reference set and showed a better absolute average fold error (AAFE) of value 1.3 compared to 1.6. A larger difference was observed in the evaluation set were the regression method and "Poulin et al." resulted in an AAFE of 1.7 and 2.6, respectively (removing the three compounds with known issues mentioned above). A similar pattern was observed for the correlation coefficient. Based on these data we suggest the regression equation method combined with a prospective confidence interval as the first choice for the extrapolation of human in vivo hepatic metabolic clearance from in vitro systems.

BDDCS, the Rule of 5 and drugability

The Rule of 5 methodology appears to be as useful today in defining drugability as when it was proposed, but recognizing that the database that we used includes only drugs that successfully reached the market. We do not view additional criteria necessary nor did we find significant deficiencies in the four Rule of 5 criteria originally proposed by Lipinski and coworkers. BDDCS builds upon the Rule of 5 and can quite successfully predict drug disposition characteristics for drugs both meeting and not meeting Rule of 5 criteria. More recent expansions of classification systems have been proposed and do provide useful qualitative and quantitative predictions for clearance relationships. However, the broad range of applicability of BDDCS beyond just clearance predictions gives a great deal of further usefulness for the combined Rule of 5/BDDCS system.

BDDCS Predictions, Self-Correcting Aspects of BDDCS Assignments, BDDCS Assignment Corrections, and Classification for more than 175 Additional Drugs

The biopharmaceutics drug disposition classification system was developed in 2005 by Wu and Benet as a tool to predict metabolizing enzyme and drug transporter effects on drug disposition. The system was modified from the biopharmaceutics classification system and classifies drugs according to their extent of metabolism and their water solubility. By 2010, Benet et al. had classified over 900 drugs. In this paper, we incorporate more than 175 drugs into the system and amend the classification of 13 drugs. We discuss current and additional applications of BDDCS, which include predicting drug-drug and endogenous substrate interactions, pharmacogenomic effects, food effects, elimination routes, central nervous system exposure, toxicity, and environmental impacts of drugs. When predictions and classes are not aligned, the system detects an error and is able to self-correct, generally indicating a problem with initial class assignment and/or measurements determining such assignments.

Few Drugs Display Flip-Flop Pharmacokinetics and These Are Primarily Associated with Classes 3 and 4 of the BDDCS

This study was conducted to determine the number of drugs exhibiting flip-flop pharmacokinetics following oral (p.o.) dosing from immediate-release dosage forms and if they exhibit a common characteristic that may be predicted based on BDDCS classification. The literature was searched for drugs displaying flip-flop kinetics (i.e., absorption half-life larger than elimination half-life) in mammals in PubMed, via internet search engines and reviewing drug pharmacokinetic data. Twenty two drugs were identified as displaying flip-flop kinetics in humans (13 drugs), rat (nine drugs), monkey (three drugs), horse (two drugs), and/or rabbit (two drugs). Nineteen of the 22 drugs exhibiting flip-flop kinetics were BDDCS Classes 3 and 4. One of the three exceptions, meclofenamic acid (Class 2), was identified in the horse; however, it would not exhibit flip-flop kinetics in humans where the p.o. dosing terminal half-life is 1.4 h. The second, carvedilol, can be explained based on solubility issues, but the third sapropterin dihydrochloride (nominally Class 1) requires further consideration. The few drugs displaying p.o. flip-flop kinetics in humans are predominantly BDDCS Classes 3 and 4. New molecular entities predicted to be BDDCS Classes 3 and 4 could be liable to exhibit flip-flop kinetics when the elimination half life is short and should be suspected to be substrates for intestinal transporters.