CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

Global Substance Registration System: consistent scientific descriptions for substances related to health.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

ConjuPepDB: a database of peptide-drug conjugates.

Peptide-drug conjugates are organic molecules composed of (i) a small drug molecule, (ii) a peptide and (iii) a linker. The drug molecule is mandatory for the biological action, however, its efficacy can be enhanced by targeted delivery, which often also reduces unwanted side effects. For site-specificity the peptide part is mainly responsible. The linker attaches chemically the drug to the peptide, but it could also be biodegradable which ensures controlled liberation of the small drug. Despite the importance of the field, there is no public comprehensive database on these species. Herein we describe ConjuPepBD, a freely available, fully annotated and manually curated database of peptide drug conjugates. ConjuPepDB contains basic information about the entries, e.g. CAS number. Furthermore, it also implies their biomedical application and the type of chemical conjugation employed. It covers more than 1600 conjugates from ∼230 publications. The web-interface is user-friendly, intuitive, and useable on several devices, e.g. phones, tablets, PCs. The webpage allows the user to search for content using numerous criteria, chemical structure and a help page is also provided. Besides giving quick insight for newcomers, ConjuPepDB is hoped to be also helpful for researchers from various related fields. The database is accessible at: https://conjupepdb.ttk.hu/.

Placental transfer and safety in pregnancy of medications under investigation to treat coronavirus disease 2019.

Objective: Treatment of coronavirus disease 2019 is mostly symptomatic, but a wide range of medications are under investigation against severe acute respiratory syndrome coronavirus 2. Although pregnant women are excluded from clinical trials, they will inevitably receive therapies whenever they seem effective in nonpregnant patients and even under compassionate use. Methods: We conducted a review of the literature on placental transfer and pregnancy safety data of drugs under current investigation for coronavirus disease 2019. Results: Regarding remdesivir, there are no data in pregnant women. Several other candidates already have safety data in pregnant women, because they are repurposed drugs already used for their established indications. Thus, they may be used in pregnancy, although their safety in the context of coronavirus disease 2019 may differ from conventional use. These include HIV protease inhibitors such as lopinavir/ritonavir that have low placental transfer, interferon that does not cross the placental barrier, and hydroxychloroquine or chloroquine that has high placental transfer. There are also pregnancy safety and placental transfer data for colchicine, steroids, oseltamivir, azithromycin, and some monoclonal antibodies. However, some drugs are strictly prohibited in pregnancy because of known teratogenicity (thalidomide) or fetal toxicities (renin-angiotensin system blockers). Other candidates including tocilizumab, other interleukin 6 inhibitors, umifenovir, and favipiravir have insufficient data on pregnancy outcomes. Conclusion: In life-threatening cases of coronavirus disease 2019, the potential risks of therapy to the fetus may be more than offset by the benefit of curing the mother. Although preclinical and placental transfer studies are required for a number of potential anti-severe acute respiratory syndrome coronavirus 2 drugs, several medications can already be used in pregnant women.

Proportion of Patients in Phase I Oncology Trials Receiving Treatments That Are Ultimately Approved.

BACKGROUND: Phase I oncology trials are often regarded as a therapeutic option for patients. However, such claims have relied on surrogate measures of benefit, such as objective response. METHODS: Using a systematic search of publications, we assessed the therapeutic value of phase I cancer trial participation by determining the probability that patients will receive active doses of treatments that eventually receive FDA approval or a National Comprehensive Cancer Network (NCCN) guideline recommendation for their indication. ClinicalTrials.gov, PubMed, American Society of Clinical Oncology reports, NCCN guidelines, and Drugs@FDA were searched between May 1, 2018, and July 31, 2018. All statistical tests were 2-sided. RESULTS: A total of 1000 phase I oncology trials initiated between 2005 and 2010 and enrolling 32 582 patients were randomly sampled from 3229 eligible trials on ClinicalTrials.gov. A total of 386 (1.2%) patients received a treatment that was approved by the US Food and Drug Administration for their malignancy at a dose delivered in the trial; including NCCN guideline recommendations, the number and proportion are 1168 (3.6%). Meta-regression showed a statistically significantly greater proportion of patients receiving a drug that was ultimately FDA approved in biomarker trials (rate ratio = 4.49, 95% confidence interval [CI] = 1.53 to 13.23; P = .006) and single-indication trials (rate ratio = 3.32, 95% CI = 1.21 to 9.15; P = .02); proportions were statistically significantly lower for combination vs monotherapy trials (rate ratio = 0.09, 95% CI = 0.01 to 0.68; P = .02). CONCLUSIONS: One in 83 patients in phase I cancer trials received a treatment that was approved for their indication at the doses received. Given published estimates of serious adverse event rates of 10%-19%, this represents low therapeutic value for phase I trial participation.

Intensive monitoring of adverse drug events associated with the use of new glucose-lowering drugs: results from an inception cohort study in Portugal.

AIMS: To determine the frequency and the time-course profile of adverse drug events associated with new glucose-lowering drugs in daily practice and to explore factors potentially associated to these events. METHODS: An inception cohort study was implemented. Adults with type 2 diabetes mellitus initiating a dipeptidyl peptidase-4 inhibitor, a glucagon-like peptide-1 receptor agonist or a sodium-glucose co-transporter-2 inhibitor were eligible for inclusion. Data were collected through baseline and follow-up telephone questionnaires, administered at 2 weeks, 3 months and 6 months. Kaplan-Meier curves and log-rank were computed to compare the time to adverse drug event onset. Cox models were used to explore potential factors associated with adverse drug events. RESULTS: A total of 1328 participants were recruited to the study. In all, 1118 adverse drug events were reported (of which 36% were not listed in the summary of product characteristics) by 41% of participants. The median latency time of adverse drug events reported in ≥1% of participants ranged from 0 to 2 days. Glucagon-like peptide-1 receptor agonist and sodium-glucose co-transporter-2 inhibitor subgroups were associated with an increased likelihood of adverse drug event reporting when compared with the dipeptidyl peptidase-4 inhibitor subgroup. A total of 328 glucose-lowering drugs were withdrawn, more than half as a result of an adverse drug event. CONCLUSIONS: More than two-fifths of participants reported an adverse drug event; dipeptidyl peptidase-4 inhibitors led to the highest proportion of unlabelled adverse drug events. Adverse drug event latency time data show that counselling and adverse drug event management should be proactively addressed from treatment initiation. There should be greater focus on prevalent new users of glucose-lowering drugs, who were more complex participants in this study in terms of type 2 diabetes disease, as they were more likely to report an adverse drug event than the incident new users.

Emerging Drug Classes and Their Potential Use in Hypertension.

Despite the availability of multiple antihypertensive drugs targeting the different pathways implicated in its pathophysiology, hypertension remains poorly controlled worldwide, and its prevalence is increasing because of the aging of the population and the obesity epidemic. Although nonadherence to treatment contributes to uncontrolled hypertension, it is likely that not all the pathophysiological mechanisms are neutralized by the various classes of antihypertensive treatment currently available, and, the counter-regulatory mechanisms triggered by these treatments may decrease their blood pressure-lowering effect. The development of new antihypertensive drugs acting on new targets, with different modes of action, therefore, remains essential, to improve blood pressure control and reduce the residual burden of cardiovascular risks further. However, the difficulties encountered in the conception, development, costs, and delivery to the market of new classes of antihypertensive agents highlights the hurdles that must be overcome to release and to evaluate their long-term safety and efficacy for hypertension only, especially because of the market pressure of cheap generic drugs. New chemical entities with blood pressure-lowering efficacy are thus being developed more for heart failure or diabetic kidney disease, 2 diseases pathophysiologically associated with hypertension. These include dual angiotensin II receptor-neprilysin inhibitors, soluble guanylate cyclase stimulators, nonsteroidal dihydropyridine-based mineralocorticoid receptor antagonists, as well as sodium-glucose cotransporter 2 inhibitors. However, centrally acting aminopeptidase A inhibitors and endothelin receptor antagonists have a dedicated program of development for hypertension. All these emergent drug classes and their potential use in hypertension are reviewed here.

What is new in lipid-lowering therapies in diabetes?

Diabetes can lead to a myriad of microvascular and macrovascular complications - with the leading cause of mortality in diabetes being cardiovascular disease. Low-density lipoprotein cholesterol, along with non-high-density lipoprotein cholesterol and triglycerides are proven, modifiable risk factors for cardiovascular disease. This article will focus on lipid-lowering agents in individuals with diabetes. It will summarise relevant changes in the latest guidelines for dyslipidaemia and will also review the mechanisms of action of lipid-lowering agents along with the latest cardiovascular outcomes data specific to individuals with diabetes. Older agents such as statins, ezetimibe, fibrates and nicotinic acid will be reviewed with a focus on new diabetes-specific evidence. Similarly, a relatively novel agent proprotein-convertase subtilisin-kexin type 9 will be reviewed and details around the Pharmaceutical Benefits Scheme criteria governing its usage in Australia will be reported. Finally, this review will touch on agents still on the horizon such as icosapent ethyl, high-density lipoprotein mimetics, bempedoic acid, omega-3 free fatty acids, bromodomain and extra-terminal protein inhibitors and inclisiran - a long-acting ribonucleic acid interference agent. In the appropriately selected population of individuals with diabetes, these agents can assist to improve further lipid profile and reduce cardiovascular events.

New treatment options for lipid-lowering therapy in subjects with type 2 diabetes.

Dyslipidemias represent a variety of quantitative and/or qualitative lipoprotein abnormalities. According to etiology, we distinguish primary dyslipidemias with strictly genetic background and secondary ones with their origin in other disease or pathological states. Diabetic dyslipidemia is a type of secondary dyslipidemia and plays an important role in determining the cardiovascular risk of subjects with type 2 diabetes. In these patients, insulin resistance is responsible for overproduction and secretion of atherogenic very low density lipoprotein. In addition, insulin resistance promotes the production of small dense low-density lipoprotein (LDL) and reduces high-density lipoprotein (HDL) production. Cardiovascular disease remains a leading cause of morbidity and mortality in diabetic patients. Previous results support the role for small, dense LDL particles in the etiology of atherosclerosis and their association with coronary artery disease. Moreover, lowering LDL cholesterol reduces the risk of cardiovascular death. Therefore, the European guidelines for the management of dyslipidemias recommend an LDL cholesterol goal < 100 mg/dL in diabetic subjects without cardiovascular events. Moreover, if triglycerides (TG) are elevated (> 400 mg/dL), they recommend a non-HDL cholesterol goal < 130 mg/dL in diabetic individuals without cardiovascular events. Statins are the first line of LDL-lowering therapy in diabetic patients and combined therapy with ezetimibe and statins could be useful in very high cardiovascular risk diabetic subjects. Furthermore, the effect of a fibrate as an add-on treatment to a statin could improve the lipid profile in diabetic individuals with high TG and low HDL cholesterol. Regarding new therapies, recent data from phase III trials show that proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors considerably decrease LDL cholesterol. Thus, they may be useful in diabetic patients with concomitant diseases such as familial dyslipidemia, recurrent cardiovascular events, and elevated LDL cholesterol after second drug administration in addition to maximal statin dose or statin intolerance.

Net Reclassification Index and Integrated Discrimination Index Are Not Appropriate for Testing Whether a Biomarker Improves Predictive Performance.

One of the goals of the Critical Path Institute's Predictive Safety Testing Consortium (PSTC) is to promote best practices for evaluating novel markers of drug induced injury. This includes the use of sound statistical methods. For rat studies, these practices have centered around comparing the area under the receiver-operator characteristic curve for each novel injury biomarker to those for the standard markers. In addition, the PSTC has previously used the net reclassification index (NRI) and integrated discrimination index (IDI) to assess the increased certainty provided by each novel injury biomarker when added to the information already provided by the standard markers. Due to their relatively simple interpretations, NRI and IDI have generally been popular measures of predictive performance. However recent literature suggests that significance tests for NRI and IDI can have inflated false positive rates and thus, tests based on these metrics should not be relied upon. Instead, when parametric models are employed to assess the added predictive value of a new marker, following (Pepe, M. S., Kerr, K. F., Longton, G., and Wang, Z. (2013). Testing for improvement in prediction model performance. Stat. Med. 32, 1467-1482), the PSTC recommends that likelihood based methods be used for significance testing.

Evidence-based selection of training compounds for use in the mechanism-based integrated prediction of drug-induced liver injury in man.

The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The 'MIP-DILI' project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.

Classification of Therapeutic and Experimental Drugs for Brown Adipose Tissue Activation: Potential Treatment Strategies for Diabetes and Obesity.

OBJECTIVE: Increasing efforts are being made towards pharmacologic activation of brown adipose tissue (BAT) in animals and humans for potential use in the treatment of obesity and diabetes. We and others have reported a number of animal studies using either experimental or therapeutic drugs. There are now efforts to translate these findings to human studies. The goal of this review is to evaluate the various drugs currently being used that have the potential for BAT activation. METHODS: Drugs were classified into 4 classes based on their mechanism of action. Class 1 drugs include the use of ß3 adrenoceptor agonists for BAT activation. Class 2 drugs include drugs that affect norepinephrine levels and activate BAT with the potential of reducing obesity. Class 3 includes activators of peroxisome proliferator-activated receptor-γ in pursuit of lowering blood sugar, weight loss and diabetes and finally Class 4 includes natural products and other emerging drugs with limited information on BAT activation and their effects on diabetes and weight loss. RESULTS: Class 1 drugs are high BAT activators followed by Class 2 and 3. Some of these drugs have now been extended to diabetes and obesity animal models and human BAT studies. Drugs in Class 3 are used clinically for Type 2 diabetes, but the extent of BAT involvement is unclear. CONCLUSION: Further studies on the efficacy of these drugs in diabetes and measuring their effects on BAT activation using noninvasive imaging will help in establishing a clinical role of BAT.

Multiparametric assay using HepaRG cells for predicting drug-induced liver injury.

The utility of HepaRG cells as an in vitro cell-based assay system for assessing drug-induced liver injury (DILI) risk was investigated. Seventeen DILI-positive and 15 DILI-negative drugs were selected for the assay. HepaRG cells were treated with each drug for 24h at concentrations that were 1.6-, 6.3-, 25-, and 100-fold the therapeutic maximum plasma concentration (Cmax). After treatment, the cell viability, glutathione content, caspase 3/7 activity, lipid accumulation, leakage of lactate dehydrogenase, and albumin secretion were measured. The sensitivity and specificity were calculated to assess the ability of the assay to predict DILI. Our multiparametric assay using HepaRG cells exhibited a 67% sensitivity and 73% specificity at a 100-fold concentration of Cmax and a 41% sensitivity and 87% specificity at a 25-fold concentration of Cmax. When a 25-fold Cmax cut-off was applied, approximately 70% of drugs exhibiting positive responses were classified into the high DILI risk category. HepaRG cells distinguished relatively safe drugs from their high-risk analogs. Our study indicates that HepaRG cells may be of use to (1) prioritize drug analogs, (2) analyze the mechanism of DILI, and (3) assess the risk for DILI in the early drug discovery stage.

Structure-based design of conformationally flexible reverse transcriptase inhibitors to combat resistant HIV.

Reverse transcriptase (RT) is one of the most important targets for HIV drug discovery. However, the emergence of resistant mutants has become one of the biggest challenges in HIV-1 RT drug discovery/development and attracted great research interests worldwide. It is particularly important to develop novel anti-HIV-1 RT agents that have improved potency and efficacy against the wild-type (WT) RT, but also target resistant RT forms. Previous crystal complex structures of HIV-1 RT revealed the interaction mechanism between the enzyme and inhibitors, which promoted the exploitation of inhibitor that had sufficient conformational flexibility to combat resistance. Hence, the potential flexibility of a drug should be part of the strategy considered in the early stages of designing drugs that are intended to be broadly effective against mutated targets associated with drug resistance. This review provides an overview of the state of the art in this field, including design strategies and challenges for medicinal chemists.

Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS).

Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.

[New tuberculosis drugs in resistant and multiresistant tuberculosis]. / Nuevos fármacos antituberculosos en la tuberculosis resistente y multirresistente.

Drug-resistant tuberculosis is a globally emerging problem with a rising incidence. According to the WHO in 2008, 17% of strains of Mycobacterium tuberculosis, in untreated cases were resistant to at least one drug and 3.6% were resistant to rifampicin and isoniazid, which is called multidrug-resistant tuberculosis. The problem is greater in patients previously treated and in some countries, where rates of multidrug resistance reach 60%. Approximately 5% of multidrug-resistant tuberculosis patients are also resistant to any fluoroquinolone and at least one injectable drug, being called extensively drug-resistant tuberculosis. The treatment of these forms of tuberculosis requires the use of second-line drugs, which causes higher cost, higher toxicity and a longer duration of treatment. There is a need for new compounds with efficacy and safety profiles better than those currently used to treat these forms of tuberculosis. In the last decade different drugs have being reassessed and appeared, which are at different stages of development.

Defining innovations of therapeutic interventions: a position paper by the Italian Society of Hospital Pharmacists.

Defining innovation in pharmacotherapy is complex because general definitions cannot be easily adapted to the specificity of different therapeutic areas. The Italian Society of Hospital Pharmacists has developed a position paper in which three criteria are used to recognise innovation to a new intervention: (1) clinical relevance of the trial's primary end-point; (2) evidence of superiority; (3) use of an adequate comparator in the controlled trial. Clinically relevant end-points should appear in predetermined lists by therapeutic area managed by our society; likewise, the third criterion must be documented by an authoritative therapeutic guideline recognised to be still valid. A preliminary attempt is presented to retrospectively apply these criteria, particularly the first one, to a series of 21 clinical trials; this initial experience has generated a first list of clinically relevant end-points. This approach of innovation assessment is proposed for application also in the field of medical devices.

Development of an in vitro liver toxicity prediction model based on longer term primary rat hepatocyte culture.

The assumption that compounds with similar toxic endpoints generate unique gene expression signatures has led to attempts to classify unknown compounds according to their genomic profile. However, studies reported so far have mostly been conducted in vivo. In this proof of concept study, we assessed the use of rat hepatocyte sandwich cultures in combination with a toxicogenomic classification method to generate a predictive in vitro toxicity classification model. After pre-incubation for 3 days, primary rat hepatocytes were treated for up to 9 days with 13 well known model compounds, changes in the global gene expression profile were measured and subsequently used for the establishment of a predictive classification model. A subset of 724 genes was capable of discriminating compounds with a misclassification rate of 7.5%. As a preliminary verification, the resulting classifier was applied to two blinded control compounds. The classification of compounds according to transient changes in global gene expression allowed the correct prediction independently from the knowledge of their underlying toxic mechanisms. The results of this first pilot study demonstrate the possibility of in vitro gene expression models to contribute to candidate selection early in drug discovery by improving the predictivity of toxicological studies and thereby reducing animal usage in toxicology.