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Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework – Extended Clearance Classification System (ECCS) – which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A – metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B – transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2 – metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A –renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B – transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4 – renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin − Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10 −6  cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

Potential risks associated with releases of human pharmaceuticals into the environment have become an increasingly important issue in environmental health. This concern has been driven by the widespread detection of pharmaceuticals in all aquatic compartments. Therefore, 22 pharmaceuticals, 6 metabolites and transformation products, belonging to 7 therapeutic groups, were selected to perform a review on their toxicity and environmental risk assessment (ERA) in different aquatic compartments, important issues to tackle the water framework directive (WFD). The toxicity data collected reported, with the exception of anxiolytics, at least one toxicity value for concentrations below 1 µg L−1. The results obtained for the ERA revealed risk quotients (RQs) higher than 1 in all the aquatic bodies and for the three trophic levels, algae, invertebrates and fish, posing ecotoxicological pressure in all of these compartments. The therapeutic groups with higher RQs were hormones, antiepileptics, anti-inflammatories and antibiotics. Unsurprisingly, RQs values were highest in wastewaters, however, less contaminated water bodies such as groundwaters still presented maximum values up to 91,150 regarding 17α-ethinylestradiol in fish. Overall, these results present an important input for setting prioritizing measures and sustainable strategies, minimizing their impact in the aquatic environment.

Background The Anatomical Therapeutic Chemical (ATC) classification system, proposed and maintained by the World Health Organization, is among the most widely used drug classification schemes. Recently, it has become a key research focus in drug repositioning. Computational models often pair drugs with ATC codes to explore drug-ATC code associations. However, the limited information available for ATC codes constrains these models, leaving significant room for improvement. Results This study presents an inference method to identify highly related target proteins, structural features, and side effects for each ATC code, constructing comprehensive biological profiles. Association networks for target proteins, structural features, and side effects are established, and a random walk with restart algorithm is applied to these networks to extract raw associations. A permutation test is then conducted to exclude false positives, yielding robust biological profiles for ATC codes. These profiles are used to construct new ATC code kernels, which are integrated with ATC code kernels from the existing model PDATC-NCPMKL. The recommendation matrix is subsequently generated using the procedures of PDATC-NCPMKL. Cross-validation results demonstrate that the new model achieves AUROC and AUPR values exceeding 0.96. Conclusion The proposed model outperforms PDATC-NCPMKL and other previous models. Analysis of the contributions of the newly added ATC code kernels confirms the value of biological profiles in enhancing the prediction of drug-ATC code associations.

Physiologically based pharmacokinetic (PBPK) models are increasingly used to predict food effect (FE) but model parameterization is challenged by in vitro-in vivo (IVIV) disconnect and/or parameter nonidentifiability. To overcome these issues, we propose a simplified PBPK model, in which all solubility-driven processes are lumped into a single parameter, solubility, which is optimized against observed concentration-time data. A set of commercially available biopharmaceutical classification system (BCS) II/IV compounds was selected to measure the solubility in a fasted state simulated intestinal fluid (FaSSIF) medium. The compounds were ranked from the lowest to the highest dose-adjusted FaSSIF solubility (FaSSIF/D) value and subdivided into three areas based on an upper and a lower limit: drugs with FaSSIF/D > upper limit having no FE, drugs with FaSSIF/D < lower limit having FE, and drugs between the limits said to be in the sensitivity range (SR), for which we tested the hypothesis that solubility-limited absorption (SLA) identified by simplified PBPK model can reliably predict positive FE if their exposures are not impacted by gut efflux or gut metabolism. We demonstrate, using a subset of drugs within SR for which PBPK models were available, that drugs with SLA exhibited a positive FE, while those with no SLA did not show FE. This proposal allows for a reliable binary prediction of FE to enable timely decisions on the need for pilot FE studies as well as the timing of pivotal FE studies.

The Biopharmaceutical Classification System (BCS) has been a prognostic tool for assessing the potential effects of formulation on the human drug oral bioavailability. When used in conjunction with in vitro dissolution tests, the BCS can support the prediction of in vivo product performance and the development of mechanistic models that support formulation assessments through the generation of “what if” scenarios. To date, the applicability of existing human BCS criteria has not been evaluated in dogs, thereby limiting its use in canine drug development. Therefore, we examined 50 drugs for which absolute bioavailability (F) was available both in dogs and humans. The drugs were also evaluated for any potential association between solubility (calculated from the dose number, Do) or lipophilicity (LogP) and F in dogs. In humans, solubility is determined in 250 mL of fluid. However, the appropriate volume for classifying drug solubility in dogs has not been established. In this analysis, the estimated volume of a water flush administered to fasted dogs (6 mL) and a volume of 250 mL scaled to a Beagle dog (35 mL) were examined. In addition, in humans, a Do value greater than 1.0 is used to define a compound as highly soluble and a LogP value greater than 1.72 as high permeability. These same criteria were applied for defining highly soluble and highly permeable in dogs. Whether using 35 or 6 mL to determine Do, the canine solubility classification remained unchanged for all but seven compounds. There were no clear associations between a drug’s F in dogs and humans or between the canine value of F and either its human BCS classification, its LogP value, or the canine Do estimate. There was a tendency for those drugs with canine values of F equal to or greater than 80% to have LogP values equal to or greater than 1.0. Exceptions to this observation tended to be those compounds known to be absorbed via mechanisms other than passive diffusion (e.g., via transporters or paracellular transporters). Although there are limitations to the approach used in this study, the results of our assessment strongly suggest that the human BCS classification system requires substantial modification before it can be reliably applied to dogs.

Many drugs are nature derived. Low drug productivity has renewed interest in natural products as drug-discovery sources. Nature-derived drugs are composed of dozens of molecular scaffolds generated by specific secondary-metabolite gene clusters in selected species. It can be hypothesized that drug-like structures probably are distributed in selective groups of species. We compared the species origins of 939 approved and 369 clinical-trial drugs with those of 119 preclinical drugs and 19,721 bioactive natural products. In contrast to the scattered distribution of bioactive natural products, these drugs are clustered into 144 of the 6,763 known species families in nature, with 80% of the approved drugs and 67% of the clinical-trial drugs concentrated in 17 and 30 drug-prolific families, respectively. Four lines of evidence from historical drug data, 13,548 marine natural products, 767 medicinal plants, and 19,721 bioactive natural products suggest that drugs are derived mostly from preexisting drug-productive families. Drug-productive clusters expand slowly by conventional technologies. The lack of drugs outside drug-productive families is not necessarily the result of under-exploration or late exploration by conventional technologies. New technologies that explore cryptic gene clusters, pathways, interspecies crosstalk, and high-throughput fermentation enable the discovery of novel natural products. The potential impact of these technologies on drug productivity and on the distribution patterns of drug-productive families is yet to be revealed.

Idiosyncratic drug-induced liver injury (DILI) is an underreported and underestimated adverse drug reaction. Information on the documented hepatotoxicity of drugs has recently been made available by a website that can be accessed in the public domain: LiverTox (http://livertox.nlm.nih.gov). According to critical analysis of the hepatotoxicity of drugs in LiverTox, 53% of drugs had at least one case report of convincing reports of liver injury. Only 48 drugs had more than 50 case reports of DILI. Amoxicillin-clavulanate is the most commonly implicated agent leading to DILI in the prospective series. In a recent prospective study, liver injury due to amoxicillin-clavulanate was found to occur in approximately one out of 2300 users. Drugs with the highest risk of DILI in this study were azathioprine and infliximab.

Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, LogP, and LogD7.4 when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated LogP, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (N = 53 compared with over 200 each in classes 1–3). The influence of several measured and in silico parameters in the process of BDDCS category assignment is discussed in detail.

Background Anticholinergic drugs are often involved in explicit criteria for inappropriate prescribing in older adults. Several scales were developed for screening of anticholinergic drugs and estimation of the anticholinergic burden. However, variation exists in scale development, in the selection of anticholinergic drugs, and the evaluation of their anticholinergic load. This study aims to systematically review existing anticholinergic risk scales, and to develop a uniform list of anticholinergic drugs differentiating for anticholinergic potency. Methods We performed a systematic search in MEDLINE. Studies were included if provided (1) a finite list of anticholinergic drugs; (2) a grading score of anticholinergic potency and, (3) a validation in a clinical or experimental setting. We listed anticholinergic drugs for which there was agreement in the different scales. In case of discrepancies between scores we used a reputed reference source (Martindale: The Complete Drug Reference®) to take a final decision about the anticholinergic activity of the drug. Results We included seven risk scales, and evaluated 225 different drugs. Hundred drugs were listed as having clinically relevant anticholinergic properties (47 high potency and 53 low potency), to be included in screening software for anticholinergic burden. Conclusion Considerable variation exists among anticholinergic risk scales, in terms of selection of specific drugs, as well as of grading of anticholinergic potency. Our selection of 100 drugs with clinically relevant anticholinergic properties needs to be supplemented with validated information on dosing and route of administration for a full estimation of the anticholinergic burden in poly-medicated older adults.