DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

NCATS Inxight Drugs: a comprehensive and curated portal for translational research.

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API.

PROTAC-DB: an online database of PROTACs.

Proteolysis-targeting chimeras (PROTACs), which selectively degrade targeted proteins by the ubiquitin-proteasome system, have emerged as a novel therapeutic technology with potential advantages over traditional inhibition strategies. In the past few years, this technology has achieved substantial progress and two PROTACs have been advanced into phase I clinical trials. However, this technology is still maturing and the design of PROTACs remains a great challenge. In order to promote the rational design of PROTACs, we present PROTAC-DB, a web-based open-access database that integrates structural information and experimental data of PROTACs. Currently, PROTAC-DB consists of 1662 PROTACs, 202 warheads (small molecules that target the proteins of interest), 65 E3 ligands (small molecules capable of recruiting E3 ligases) and 806 linkers, as well as their chemical structures, biological activities, and physicochemical properties. Except the biological activities of warheads and E3 ligands, PROTAC-DB also provides the degradation capacities, binding affinities and cellular activities for PROTACs. PROTAC-DB can be queried with two general searching approaches: text-based (target name, compound name or ID) and structure-based. In addition, for the convenience of users, a filtering tool for the searching results based on the physicochemical properties of compounds is also offered. PROTAC-DB is freely accessible at http://cadd.zju.edu.cn/protacdb/.

Liver-targeting drugs and their effect on blood glucose and hepatic lipids.

The global epidemic of non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH) and the high prevalence among individuals with type 2 diabetes has attracted the attention of clinicians specialising in liver disorders. Many drugs are in the pipeline for the treatment of NAFLD/NASH, and several glucose-lowering drugs are now being tested specifically for the treatment of liver disease. Among these are nuclear hormone receptor agonists (e.g. peroxisome proliferator-activated receptor agonists, farnesoid X receptor agonists and liver X receptor agonists), fibroblast growth factor-19 and -21, single, dual or triple incretins, sodium-glucose cotransporter inhibitors, drugs that modulate lipid or other metabolic pathways (e.g. inhibitors of fatty acid synthase, diacylglycerol acyltransferase-1, acetyl-CoA carboxylase and 11ß-hydroxysteroid dehydrogenase type-1) or drugs that target the mitochondrial pyruvate carrier. We have reviewed the metabolic effects of these drugs in relation to improvement of diabetic hyperglycaemia and fatty liver disease, as well as peripheral metabolism and insulin resistance.

Prediction of Pregnancy-Induced Changes in Secretory and Total Renal Clearance of Drugs Transported by Organic Anion Transporters.

Pregnancy can significantly change the pharmacokinetics of drugs, including those renally secreted by organic anion transporters (OATs). Quantifying these changes in pregnant women is logistically and ethically challenging. Hence, predicting the in vivo plasma renal secretory clearance (CLsec) and renal CL (CLrenal) of OAT drugs in pregnancy is important to design correct dosing regimens of OAT drugs. Here, we first quantified the fold-change in renal OAT activity in pregnant versus nonpregnant individual using available selective OAT probe drug CLrenal data (training dataset; OAT1: tenofovir, OAT2: acyclovir, OAT3: oseltamivir carboxylate). The fold-change in OAT1 activity during the 2nd and 3rd trimester was 2.9 and 1.0 compared with nonpregnant individual, respectively. OAT2 activity increased 3.1-fold during the 3rd trimester. OAT3 activity increased 2.2, 1.7 and 1.3-fold during the 1st, 2nd, and 3rd trimester, respectively. Based on these data, we predicted the CLsec, CLrenal and total clearance ((CLtotal) of drugs in pregnancy, which are secreted by multiple OATs (verification dataset; amoxicillin, pravastatin, cefazolin and ketorolac, R-ketorolac, S-ketorolac). Then, the predicted clearances (CLs) were compared with the observed values. The predicted/observed CLsec, CLrenal, and CLtotal of drugs in pregnancy of all verification drugs were within 0.80-1.25 fold except for CLsec of amoxicillin in the 3rd trimester (0.76-fold) and cefazolin in the 2nd trimester (1.27-fold). Overall, we successfully predicted the CLsec, CLrenal, and CLtotal of drugs in pregnancy that are renally secreted by multiple OATs. This approach could be used in the future to adjust dosing regimens of renally secreted OAT drugs which are administered to pregnant women. SIGNIFICANCE STATEMENT: To the authors' knowledge, this is the first report to successfully predict renal secretory clearance and renal clearance of multiple OAT substrate drugs during pregnancy. The data presented here could be used in the future to adjust dosing regimens of renally secreted OAT drugs in pregnancy. In addition, the mechanistic approach used here could be extended to drugs transported by other renal transporters.

Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network.

The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fields of medicinal chemistry and drug industries have been a profound scientific development, since only a few amounts of the small molecule libraries were identified as approvable drugs. In this study, a deep belief network was utilized to construct a druglikeness classification model. For this purpose, small molecules and approved drugs from the ZINC database were selected for the unsupervised pre-training step and supervised training step. Various binary fingerprints such as Macc 166 bit, PubChem 881 bit, and Morgan 2048 bit as data features were investigated. The report revealed that using an unsupervised pre-training phase can lead to a good performance model and generalizability capability. Accuracy, precision, and recall of the model for Macc features were 97%, 96%, and 99%, respectively. For more consideration about the generalizability of the model, the external data by expression and investigational drugs in drug banks as drug data and randomly selected data from the ZINC database as non-drug were created. The results confirmed the good performance and generalizability capability of the model. Also, the outcomes depicted that a large proportion of misclassified non-drug small molecules ascertain the bioavailability conditions and could be investigated as a drug in the future. Furthermore, our model attempted to tap potential opportunities as a drug filter in drug discovery.

Data science-driven analyses of drugs inducing hypertension as an adverse effect.

The utilization of approved medication is a requisite to combat certain diseases for health; however, the undesirable adverse effects (AEs) due to medication are generally unavoidable. Hypertension is one of such AEs resulting from approved medication in which blood pressure in the arteries gets elevated and is a risk factor for several diseases including heart and kidney failure. HTs are the approved drugs that can cause hypertension as an AE. Here, the goal of the study is to investigate the structural and functional diversities of HTs. In our quest to unravel the structural parameters of the HTs, a systematic analysis of the HTs having a different number and type of ring systems was conducted. The cellular component, molecular function and biological processes adopted by the gene products were analyzed. Moreover, our systematically done analysis suggests that all the target families are active in a common pathway, that is, nerve transmission. A comparison of the selected structural and functional aspect of HTs with anti-hypertensives suggests that HTs follow certain structural and functional features in spite of many possibilities. Our study provides a promising methodology that considers the influence of structural diversity of AE causing agents on a functional perspective for precursory clinical decision making. This could be extended to explore the structural and functional trends that are adopted by agents causing certain diseases or AEs.

DrugCentral 2018: an update.

DrugCentral is a drug information resource (http://drugcentral.org) open to the public since 2016 and previously described in the 2017 Nucleic Acids Research Database issue. Since the 2016 release, 103 new approved drugs were updated. The following new data sources have been included: Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), FDA Orange Book information, L1000 gene perturbation profile distance/similarity matrices and estimated protonation constants. New and existing entries have been updated with the latest information from scientific literature, drug labels and external databases. The web interface has been updated to display and query new data. The full database dump and data files are available for download from the DrugCentral website.

Scientific considerations to move towards biowaiver for biopharmaceutical classification system class III drugs: How modeling and simulation can help.

The 2017 Guidance by U.S. Food and Drug Administration (FDA) has recommended the criteria to qualify for a Biopharmaceutical Classification System (BCS)-based biowaiver that includes high solubility of the drug across the physiological pH range as well as the formulation considerations, e.g., being qualitatively the same and quantitatively very similar to the reference product. These were ratified by the International Council for Harmonization (ICH) in 2018. The FDA has used the similar verbiage when referring to the BCS-based biowaiver option for BCS class III drugs (highly soluble but poorly permeable). However, establishing in vitro-in vivo correlations (IVIVC) using conventional mass balance deconvolution approaches, which assumes a single absorption compartment, is not likely for very rapidly dissolving dosage forms containing BCS III drugs. Unlike conventional mass balance deconvolution techniques, physiologically based pharmacokinetic models are able to disentangle different processes contributing to the input function, e.g., dissolution, gastrointestinal transit, and permeation and to establish IVIVC using variants of the compartmental absorption and transit model, supporting biowaiver for formulations containing BCS III drugs. However, there are knowledge gaps that need to be filled. This review provides a systematic assessment of the advancements in applications of physiologically based pharmacokinetic (PBPK) models for IVIVC and biowaiver for such cases with the aim of identifying the most important gaps and hurdles. It concludes by calling for research efforts on the impact of excipients on dissolution and permeation, alongside the development of PBPK modeling to link these in vitro characteristics to in vivo bioequivalence outcomes through simulations of virtual clinical studies.

Drug Use Disorders and Violence: Associations With Individual Drug Categories.

We conducted a systematic review that examined the link between individual drug categories and violent outcomes. We searched for primary case-control and cohort investigations that reported risk of violence against others among individuals diagnosed with drug use disorders using validated clinical criteria, following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We identified 18 studies published during 1990-2019, reporting data from 591,411 individuals with drug use disorders. We reported odds ratios of the violence risk in different categories of drug use disorders compared with those without. We found odds ratios ranging from 0.8 to 25.0 for most individual drug categories, with generally higher odds ratios among individuals with polydrug use disorders. In addition, we explored sources of between-study heterogeneity by subgroup and meta-regression analyses. Cohort investigations reported a lower risk of violence than case-control reports (odds ratio =  2.7 (95% confidence interval (CI): 2.1, 3.5) vs. 6.6 (95% CI: 5.1, 8.6)), and associations were stronger when the outcome was any violence rather than intimate partner violence (odds ratio = 5.7 (95% CI: 3.8, 8.6) vs. 1.7 (95% CI: 1.4, 2.1)), which was consistent with results from the meta-regression. Overall, these findings highlight the potential impact of preventing and treating drug use disorders on reducing violence risk and associated morbidities.

Medications for pregnant women: A balancing act between the interests of the mother and of the fetus.

Drug entry into the adult brain is controlled by efflux mechanisms situated in various brain barrier interfaces. The effectiveness of these protective mechanisms in the embryo, fetus and newborn brain is less clear. The longstanding belief that "the" blood-brain barrier is absent or immature in the fetus and newborn has led to many misleading statements with potential clinical implications. Here we review the properties of brain barrier mechanisms in the context of drug entry into the developing brain and discuss the limited number of studies published on the subject. We noticed that most of available literature suffers from some experimental limitations, notably that drug levels in fetal blood and cerebrospinal fluid have not been measured. This means that the relative contribution to the overall brain protection provided by individual barriers such as the placenta (which contains similar efflux mechanisms) and the brain barriers cannot be separately ascertained. Finally, we propose that systematic studies in appropriate animal models of drug entry into the brain at different stages of development would provide a rational basis for use of medications in pregnancy and in newborns, especially prematurely born, where protection usually provided by the placenta is no longer present.

Delayed Hypersensitivity Reactions Caused by Drug Excipients: A Literature Review.

The European Medicines Agency (EMA) defines excipients as the constituents of a pharmaceutical form apart from the active substance. Delayed hypersensitivity reactions (DHRs) caused by excipients contained in the formulation of medications have been described. However, there are no data on the prevalence of DHRs due to drug excipients. Clinical manifestations of allergy to excipients can range from skin disorders to life-threatening systemic reactions. The aim of this study was to perform a literature review on allergy to pharmaceutical excipients and to record the DHRs described with various types of medications, specifically due to the excipients contained in their formulations. The cases reported were sorted alphabetically by type of medication and excipient, in order to obtain a list of the excipients most frequently involved for each type of medication.

Pharmacology for Preterm Labor.

Preterm birth occurs with 10% of deliveries and yet accounts for more than 85% of perinatal morbidity and mortality. Management of preterm labor prior to delivery includes a multipronged pharmacologic approach targeting utilization of reproductive hormones for continuation of pregnancy, advancement of fetal lung maturity, and the decrease of uterine contractility (tocolysis). This article will review and compare guidelines on pharmacologic management of preterm labor as recommended by the American College of Obstetricians and Gynecologists and the European Association of Perinatal Medicine. The classifications of drugs discussed include exogenous progesterone, corticosteroids, and tocolytics (ß-adrenergic agonists, magnesium sulfate, calcium channel blockers, prostaglandin inhibitors, nitrates, and oxytocin receptor blockers). For each of these drug classes, the following information will be presented: mechanism of action, maternal/fetal side effects, and nursing implications.

Selected Pharmaceuticals in Different Aquatic Compartments: Part II-Toxicity and Environmental Risk Assessment.

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.

Metabolic-Hydroxy and Carboxy Functionalization of Alkyl Moieties in Drug Molecules: Prediction of Structure Influence and Pharmacologic Activity.

Alkyl moieties-open chain or cyclic, linear, or branched-are common in drug molecules. The hydrophobicity of alkyl moieties in drug molecules is modified by metabolic hydroxy functionalization via free-radical intermediates to give primary, secondary, or tertiary alcohols depending on the class of the substrate carbon. The hydroxymethyl groups resulting from the functionalization of methyl groups are mostly oxidized further to carboxyl groups to give carboxy metabolites. As observed from the surveyed cases in this review, hydroxy functionalization leads to loss, attenuation, or retention of pharmacologic activity with respect to the parent drug. On the other hand, carboxy functionalization leads to a loss of activity with the exception of only a few cases in which activity is retained. The exceptions are those groups in which the carboxy functionalization occurs at a position distant from a well-defined primary pharmacophore. Some hydroxy metabolites, which are equiactive with their parent drugs, have been developed into ester prodrugs while carboxy metabolites, which are equiactive to their parent drugs, have been developed into drugs as per se. In this review, we present and discuss the above state of affairs for a variety of drug classes, using selected drug members to show the effect on pharmacologic activity as well as dependence of the metabolic change on drug molecular structure. The review provides a basis for informed predictions of (i) structural features required for metabolic hydroxy and carboxy functionalization of alkyl moieties in existing or planned small drug molecules, and (ii) pharmacologic activity of the metabolites resulting from hydroxy and/or carboxy functionalization of alkyl moieties.

Do disease-modifying drugs (DMD) have a positive impact on the occurrence of secondary progressive multiple sclerosis? No.

RR MS evolution has changed since the beginning of the availability of MS disease-modifying drugs (DMD). Before concluding a unique impact of the efficiency of DMD, careful analysis of long-term studies has to be conducted. Analysis of the literature points out a few bias in the long-term follow of MS patients under DMD: indication of DMD has changed since 20 years, diagnosis criteria are not the same (including the Will Rogers phenomen), and so far population are not homogeneous and comparable. Analysis criteria of the efficiency of the treatments are not the same, pending on the date of the publications. References concerning the long-term impact of DMD are in fact very limited. In addition, long-term efficiency of 2nd line treatments is not available. Another explanation of the change of MS evolution could be the lower evolutivity of MS patients since 2 decades. Analysis of placebo group in pivotal studies, argues to a decrease of the relapse annual rate and mean EDSS score in the more recent studies and recent MS diagnosed patients. To conclude, long-term evolution of MS patients is more favorable, influence of DMD is likely, but not unique.

Do disease-modifying drugs (DMD) have a positive impact on the occurrence of secondary progressive multiple sclerosis? Comment.

Very recent data from cohorts, such as that of the French Observatory of Multiple Sclerosis (OFSEP) and the MSBase cohort, are the subject of new statistical analyses using propensity scores that enable the matching of relapses frequency, EDSS, age, and sex ratio in patient populations for comparisons with each other, which reduces selection biases. The first data from these cohorts revealed a decline in transition to secondary progressive MS with the most effective disease-modifying drugs currently available, especially when these drugs were used early in the disease. However, these studies remain limited regarding the number of patients, the duration of follow-up, the use of imperfect methodologies, and the level of evidence remains low. The Gothenburg cohort in Sweden, which has been followed since the 1950s, found that 14% of benign non-progressive multiple sclerosis (MS) never evolved to secondary progression after more than 45 years of evolution. EDSS 7 was reached after 48 years of disease (median), and 50% evolved to secondary progressive MS after 15 years (consistent with data from the historic London, Ontario cohort). These data demonstrate that most people living with MS evolve without treatment to a significant long-term disability and that this evolution is closely linked to secondary progression (more than the relapse frequency). Benign forms appear as MS that never passes into secondary progressive MS. Recent data demonstrate that the delay until transition to secondary progression (more than 30 years in the MSBase cohort) and the delay in reaching EDSS 6 decreased since the introduction of disease-modifying drugs 20 years ago. However, randomized placebo-controlled trials do not last more than 2 or 3 years, and many biases may be involved in long-term follow-up studies: worsening patients who are lost to follow-up ("informative censoring" bias: only good responders to treatment remain primarily under the same long-term treatment and are followed); changes in the populations in the most recent studies with a lower rate of relapse and lower progression of disability at the beginning of the disease prior to initiating treatments; and environmental changes that remain largely misunderstood and may contribute to a natural evolution towards less severe disease.

Do disease-modifying drugs (DMD) have a positive impact on the occurrence of secondary progressive multiple sclerosis? Yes.

During the 20 past years, the management of multiple sclerosis (MS) has largely changed especially concerning therapeutical approach. Before 1996, treatments were restricted to corticosteroids for relapses, several symptomatic treatments and unselective immunosuppressive drugs (azathioprine, cyclophosphamide, methotrexate) with a low evidence of any efficacy. In the present review, we analyze the principal real-life cohorts of MS during several periods (before therapeutical modern area, first-generation treatment area and most recent period). Despite many methodological problems, we observe globally a delay of around 3-5 years between untreated cohorts and first-generation treatments for going to EDSS 6 which is probably the most robust score. This delay is clearly increase to at least 15 years with the most recent cohort treated first and second-line treatments confirming that early and more intensive treatment are necessary to have a long-term efficacy on disability progression and especially on severe disability represent by EDSS 6. Larger cohorts with longer follow-up is necessary to confirm these tendencies and OFSEP observatory or MS base will probably provide us the possibility to conclude in a couple of years.

RedMed: Extending drug lexicons for social media applications.

Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media. The Reddit comment archive represents an ideal corpus for bridging this gap. We trained a word embedding model, RedMed, to facilitate the identification and retrieval of health entities from Reddit data. We compare the performance of our model trained on a consumer-generated corpus against publicly available models trained on expert-generated corpora. Our automated classification pipeline achieves an accuracy of 0.88 and a specificity of >0.9 across four different term classes. Of all drug mentions, an average of 79% (±0.5%) were exact matches to a generic or trademark drug name, 14% (±0.5%) were misspellings, 6.4% (±0.3%) were synonyms, and 0.13% (±0.05%) were pill marks. We find that our system captures an additional 20% of mentions; these would have been missed by approaches that rely solely on exact string matches. We provide a lexicon of misspellings and synonyms for 2978 drugs and a word embedding model trained on a health-oriented subset of Reddit.

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.