The 10th International Congress on Cutaneous Adverse Drug Reactions, Shimane, Japan, 2018: Focus on New Discoveries.

Drug Saf. 2019 06;42(6):797-801

Authors: Olteanu C, Shear NH, Morita E, Chung WH, Niihara H, Matsukura S, Hashimoto R, Dodiuk-Gad RP

PMID: 31037651 [PubMed - indexed for MEDLINE]

ADMET modeling approaches in drug discovery.

Drug Discov Today. 2019 05;24(5):1157-1165

Authors: Ferreira LLG, Andricopulo AD

Abstract
In silico prediction of ADMET is an important component of pharmaceutical R&D. Last year, the FDA approved 59 new molecular entities, with small molecules comprising 64% of the therapies approved in 2018. Estimation of pharmacokinetic properties in the early phases of drug discovery has been central to guiding hit-to-lead and lead-optimization efforts. Given the outstanding complexity of the current R&D model, drug discovery players have intensely pursued molecular modeling strategies to identify patterns in ADMET data and convert them into knowledge. The field has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.

PMID: 30890362 [PubMed - indexed for MEDLINE]

A Comparison Study of Algorithms to Detect Drug-Adverse Event Associations: Frequentist, Bayesian, and Machine-Learning Approaches.

Drug Saf. 2019 06;42(6):743-750

Authors: Pham M, Cheng F, Ramachandran K

Abstract
INTRODUCTION: It is important to monitor the safety profile of drugs, and mining for strong associations between drugs and adverse events is an effective and inexpensive method of post-marketing safety surveillance.
OBJECTIVE: The objective of our work was to compare the accuracy of both common and innovative methods of data mining for pharmacovigilance purposes.
METHODS: We used the reference standard provided by the Observational Medical Outcomes Partnership, which contains 398 drug-adverse event pairs (165 positive controls, 233 negative controls). Ten methods and algorithms were applied to the US FDA Adverse Event Reporting System data to investigate the 398 pairs. The ten methods include popular methods in the pharmacovigilance literature, newly developed pharmacovigilance methods as at 2018, and popular methods in the genome-wide association study literature. We compared their performance using the receiver operating characteristic (ROC) plot, area under the curve (AUC), and Youden's index.
RESULTS: The Bayesian confidence propagation neural network had the highest AUC overall. Monte Carlo expectation maximization, a method developed in 2018, had the second highest AUC and the highest Youden's index, and performed very well in terms of high specificity. The regression-adjusted gamma Poisson shrinkage model performed best under high-sensitivity requirements.
CONCLUSION: Our results will be useful to help choose a method for a given desired level of specificity. Methods popular in the genome-wide association study literature did not perform well because of the sparsity of data and will need modification before their properties can be used in the drug-adverse event association problem.

PMID: 30762164 [PubMed - indexed for MEDLINE]

A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.

Drug Saf. 2019 06;42(6):721-725

Authors: McMaster C, Liew D, Keith C, Aminian P, Frauman A

Abstract
INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10th Revision (ICD-10) codes have been used in epidemiological studies of ADRs and offer the potential for automated ADR detection systems.
OBJECTIVE: The aim of this study was to develop an automated ADR detection system based on ICD-10 codes, using machine-learning algorithms to improve accuracy and efficiency.
METHODS: For a 12-month period from December 2016 to November 2017, every inpatient episode receiving an ICD-10 code in the range Y40.0-Y59.9 (ADR code) was flagged for review as a potential ADR. Each flagged admission was assessed by an expert pharmacist and, if needed, reviewed at regular ADR committee meetings. For each report, a determination was made about ADR probability and severity. The dataset was randomly split into training and test sets. A machine-learning model using the random forest algorithm was developed on the training set to discriminate between true and false ADR reports. The model was then applied to the test set to assess accuracy using the area under the receiver operating characteristic (AUC).
RESULTS: In the study period, 2917 Y40.0-Y59.9 codes were applied to admissions, resulting in 245 ADR reports after review. These 245 reports accounted for 44.5% of all ADR reporting in our hospital in the study period. A random forest model built on the training set was able to discriminate between true and false reports on the test set with an AUC of 0.803.
CONCLUSIONS: Automated ADR detection using ICD-10 coding significantly improved ADR detection in the study period, with improved discrimination between true and false reports by applying a machine-learning model.

PMID: 30725336 [PubMed - indexed for MEDLINE]

Exploring Joint AB-LSTM With Embedded Lemmas for Adverse Drug Reaction Discovery.

IEEE J Biomed Health Inform. 2019 09;23(5):2148-2155

Authors: Santiso S, Perez A, Casillas A

Abstract
This work focuses on the detection of adverse drug reactions (ADRs) in electronic health records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients' safety. The fact is that ADRs tend to be under-reported in daily hospital praxis. In this context, automatic solutions based on text mining can help to alleviate the workload of experts. Nevertheless, these solutions pose two challenges: 1) EHRs show high lexical variability, the characterization of the events must be able to deal with unseen words or contexts and 2) ADRs are rare events, hence, the system should be robust against skewed class distribution. To tackle these challenges, deep neural networks seem appropriate because they allow a high-level representation. Specifically, we opted for a joint AB-LSTM network, a sub-class of the bidirectional long short-term memory network. Besides, in an attempt to reinforce lexical variability, we proposed the use of embeddings created using lemmas. We compared this approach with supervised event extraction approaches based on either symbolic or dense representations. Experimental results showed that the joint AB-LSTM approach outperformed previous approaches, achieving an f-measure of 73.3.

PMID: 30403644 [PubMed - indexed for MEDLINE]

Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database.

IEEE J Biomed Health Inform. 2019 09;23(5):2156-2163

Authors: Chasioti D, Yao X, Zhang P, Lerner S, Quinney SK, Ning X, Li L, Shen L

Abstract
Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our paper provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin, and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

PMID: 30296244 [PubMed - indexed for MEDLINE]

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Platelet Function Test Use for Patients with Coronary Artery Disease in the Early 2020s.

J Clin Med. 2020 Jan 10;9(1):

Authors: Fontana P, Roffi M, Reny JL

Abstract
In the field of antithrombotics, precision medicine is of particular interest, as it may lower the incidence of potentially life-threatening side effects. Indeed, antiplatelet drugs such as P2Y12 inhibitors are one of the most common causes of emergency admissions for drug-related adverse events. The last ten years have seen a continuous debate on whether platelet function tests (PFTs) should be used to tailor antiplatelet drugs to cardiovascular patients. Large-scale randomized studies investigating the escalation of antiplatelet therapies according to the results of PFTs were mostly negative. Potent P2Y12 inhibitors are recommended as a first-line treatment in acute coronary syndrome patients, bringing the bleeding risk at the forefront. De-escalation from prasugrel or ticagrelor to clopidogrel is now considered, with or without the use of a PFT. This review covers recent advances in escalation and de-escalation strategies based on PFTs in various clinical settings. It also describes the main features of the most popular platelet function tests as well as the potential added value of genetic testing. Finally, we detail practical suggestions on how PFTs could be used in clinical practice.

PMID: 31936845 [PubMed]

Critical View on the Usage of Ribavirin in Already Existing Psychostimulant-Use Disorde.

Curr Pharm Des. 2020 Jan 14;:

Authors: Petković B, Kesić S, Pešić V

Abstract
Substance-use disorder represents a frequently hidden non-communicable chronic disease. Patients with intravenous drug addiction are at high risk of direct exposure to a variety of viral infections and are considered to be the largest subpopulation infected with the hepatitis C virus. Ribavirin is a synthetic nucleoside analog that has been used as an integral component of hepatitis C therapy. However, ribavirin medication is quite often associated with pronounced psychiatric adverse effects. It is not well understood to what extent ribavirin per se contributes to changes in drug-related neurobehavioral disturbances, especially in the case of psychostimulant drugs such as amphetamine. It is now well-known that repeated amphetamine usage produces psychosis in humans and behavioral sensitization in animals. On the other hand, ribavirin has an affinity for adenosine A1 receptors that antagonistically modulate the activity of dopamine D1 receptors, which play a critical role in the development of behavioral sensitization. This review will focus on the current knowledge of neurochemical/neurobiological changes that exist in the psychostimulant drug-addicted brain itself and the antipsychotic-like efficiency of adenosine agonists. Particular attention will be paid to the potential side effects of ribavirin therapy, and the opportunities and challenges related to its application in already existing psychostimulant-use disorder.

PMID: 31939725 [PubMed - as supplied by publisher]

Protective effects of Ginkgo biloba L. against natural toxins, chemical toxicities, and radiation: A comprehensive review.

Phytother Res. 2019 Nov;33(11):2821-2840

Authors: Omidkhoda SF, Razavi BM, Hosseinzadeh H

Abstract
Nowadays in our developing and industrial world, humans' health or even their life is threatened by exposure to poisons. In this situation, detecting a protective compound could be helpful and interesting. In the present article, we collected and reviewed all studies, which have been conducted so far about the protective effects of Ginkgo biloba L. (GB), one of the most ancient medicinal tree species, against toxicities induced by chemical toxic agents, natural toxins, and also radiation. In overall, investigations showed that GB exerts the antioxidant, antiinflammatory, antiapoptotic, and antigenotoxicity effects in different toxicities. There are also some special mechanisms about its protective effects against some specific toxic agents, such as acetylcholine esterase inhibition in the aluminium neurotoxicity or membrane-bond phosphodiesterase activation in the triethyltin toxicity. Ginkgolide A was the most investigated active ingredient of G. biloba leaf extract as a protective compound against toxicities, which had the similar effects of total extract. A few clinical studies have been conducted in this field, which demonstrated the beneficial effects of GB against toxic agents. However, the promising effects of this valuable herbal extract will practically remain useless without carrying out more clinical studies and proving its effects on human beings.

PMID: 31429152 [PubMed - indexed for MEDLINE]

Académie nationale de pharmacie.

Ann Biol Clin (Paris). 2019 Jun 01;77(3):355-356

Authors:

PMID: 31219425 [PubMed - indexed for MEDLINE]

Pylera® plus ranitidine vs Pylera® plus esomeprazole in first-line treatment of Helicobacter pylori infection: Two pilot studies.

Helicobacter. 2019 Oct;24(5):e12606

Authors: Ciccaglione AF, Cellini L, Marzio L

Abstract
BACKGROUND: Several studies have shown that Pylera® (three-in-one capsules containing 140 mg bismuth potassium subcitrate, 125 metronidazole, and tetracycline 125 mg) in association with omeprazole or esomeprazole is a good option in the treatment of Helicobacter pylori infection. In particular, the adjunction of a PPI to Pylera® may be useful to overcome metronidazole resistance. However, omeprazole and its derivatives can promote greater bismuth absorption and enhance its toxicity. The H2 receptor antagonist (H2RA) ranitidine seems to induce less bismuth absorption and as a consequence less systemic toxicity.
AIM: To evaluate whether Pylera® in combination with esomeprazole or with ranitidine is equally effective in the treatment of H. pylori infection.
MATERIAL AND METHODS: Two separate groups of patients were treated simultaneously. One group was treated with Pylera® three capsules qid plus esomeprazole 40 mg bid for 10 days (group A), and the other group was treated with Pylera® three capsules qid plus ranitidine 300 mg bid for 10 days (group B). H. pylori eradication was defined as a negative result in 13 C urea breath test performed at least 8 weeks after the end of treatment with a delta-over-baseline value less than 5.
RESULTS: Thirty-two patients were recruited for group A and thirty-three patients in group B. Eradication rates were 93.7% (30/32) and 90.9% (30/33), respectively, at intention-to-treat analysis, and 96.6% (29/30) and 93.3% (28/30), respectively, at per-protocol analysis. Adverse events occurred in 26 patients and led to the suspension of treatment in one patient in group A and in one patient in group B.
CONCLUSION: The results showed that Pylera® plus a PPI or ranitidine were equally effective in the population studied. The high cure rates of bismuth triple therapy (without an antisecretory drug) and the lack of susceptibility testing make it impossible to exclude the possibility that the results would have been similar if neither the PPI nor the ranitidine were given.

PMID: 31168941 [PubMed - indexed for MEDLINE]

Incorporating User Generated Content for Drug Drug Interaction Extraction Based on Full Attention Mechanism.

IEEE Trans Nanobioscience. 2019 07;18(3):360-367

Authors: Xu B, Shi X, Yin Y, Zhao Z, Zheng W, Lin H, Yang Z, Wang J, Xia F

Abstract
It is crucial for doctors to fully understand the interaction between drugs in prescriptions, especially when a patient takes multiple medications at the same time during treatment. The purpose of drug drug interaction (DDI) extraction is to automatically obtain the interaction between drugs from biomedical literature. Current state-of-the-art approaches for DDI extraction task are based on artificial intelligence and natural language processing. While such existing DDI extraction methods can provide more knowledge and enhance the performance through external resources such as biomedical databases or ontologies, due to the difficulty of updating, these external resources are delayed. In fact, user generated content (UGC) is another kind of external medical resources that can be quickly updated. We are trying to use UGC resources to provide more available information for our deep learning DDI extraction method. In this paper, we present a DDI extraction approach through a new attention mechanism called full-attention which can combine the UGC information with contextual information. We conducted a series of experiments on the DDI 2013 Evaluation dataset to evaluate our method. Experiments show improved performance compared with the state of the art and UGC-DDI model achieves a competitive F-score of 0.712.

PMID: 31144641 [PubMed - indexed for MEDLINE]

Pharmacological and residual effects in randomized placebo-controlled trials. A structural causal modelling approach.

Rev Epidemiol Sante Publique. 2019 Jul;67(4):267-274

Authors: Mouchart M, Bouckaert A, Wunsch G

Abstract
BACKGROUND: Distinguishing between pharmacological and residual effects, this paper considers the problem of causal assessment in the case of a particular model, namely a Sure Outcome of Random Events (SORE) model developed for the analysis of data from a randomized placebo-controlled double-blind trial of a drug.
METHOD: This model takes into account two kinds of observable effects, a therapeutic effect and a side-effect. For each observable effect, two latent factors are considered, i.e. a pharmacological (or explained) factor and a residual (or unexplained) one.
RESULTS: The model presents a plausible mechanism generating the observed and latent outcomes, recursively decomposed into an ordered sequence of sub-mechanisms.
CONCLUSIONS: The characteristics of this model leads to a novel assessment of causality that evaluates the effect of latent variables and of the bias resulting from ignoring the structural features of the data generating process. This approach is illustrated by a numerical example, along with a case study based on a secondary analysis of real data.

PMID: 31056218 [PubMed - indexed for MEDLINE]

[Which follow-up for innovative treatments?]

Med Sci (Paris). 2019 Mar;35 Hors série n° 1:54-56

Authors: Campana-Salort E, Espil-Taris C, Prigent H, de Antonio M, Lebrun-Vignes B, Tiffreau V, Honnet G

PMID: 30943166 [PubMed - indexed for MEDLINE]

Profiling antimicrobial peptides from the medical maggot Lucilia sericata as potential antibiotics for MDR Gram-negative bacteria.

J Antimicrob Chemother. 2019 01 01;74(1):96-107

Authors: Hirsch R, Wiesner J, Marker A, Pfeifer Y, Bauer A, Hammann PE, Vilcinskas A

Abstract
Background: The ability of MDR Gram-negative bacteria to evade even antibiotics of last resort is a severe global challenge. The development pipeline for conventional antibiotics cannot address this issue, but antimicrobial peptides (AMPs) offer an alternative solution.
Objectives: Two insect-derived AMPs (LS-sarcotoxin and LS-stomoxyn) were profiled to assess their suitability for systemic application in humans.
Methods: The peptides were tested against an extended panel of 114 clinical MDR Gram-negative bacterial isolates followed by time-kill analysis, interaction studies and assays to determine the likelihood of emerging resistance. In further in vitro studies we addressed cytotoxicity, cardiotoxicity and off-target interactions. In addition, an in vivo tolerability and pharmacokinetic study in mice was performed.
Results: LS-sarcotoxin and LS-stomoxyn showed potent and selective activity against Gram-negative bacteria and no cross-resistance with carbapenems, fluoroquinolones or aminoglycosides. Peptide concentrations of 4 or 8 mg/L inhibited 90% of the clinical MDR isolates of Escherichia coli, Enterobacter cloacae, Acinetobacter baumannii and Salmonella enterica isolates tested. The 'all-d' homologues of the peptides displayed markedly reduced activity, indicating a chiral target. Pharmacological profiling revealed a good in vitro therapeutic index, no cytotoxicity or cardiotoxicity, an inconspicuous broad-panel off-target profile, and no acute toxicity in mice at 10 mg/kg. In mouse pharmacokinetic experiments LS-sarcotoxin and LS-stomoxyn plasma levels above the lower limit of quantification (1 and 0.25 mg/mL, respectively) were detected after 5 and 15 min, respectively.
Conclusions: LS-sarcotoxin and LS-stomoxyn are suitable as lead candidates for the development of novel antibiotics; however, their pharmacokinetic properties need to be improved for systemic administration.

PMID: 30272195 [PubMed - indexed for MEDLINE]

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

Pharmaceut Med. 2019 Apr;33(2):109-120

Authors: Mockute R, Desai S, Perera S, Assuncao B, Danysz K, Tetarenko N, Gaddam D, Abatemarco D, Widdowson M, Beauchamp S, Cicirello S, Mingle E

Abstract
INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life.
AIM: The aim of this study was to identify areas across the PV value chain that can be augmented by cognitive service solutions using the methodologies of contextual analysis and cognitive load theory. It will also provide a framework of how to validate these PV cognitive services leveraging the acceptable quality limit approach.
METHODS: The data used to train the cognitive service were an annotated corpus consisting of 20,000 ICSRS from which we developed a framework to identify and validate 40 cognitive services ranging from information extraction to complex decision making. This framework addresses the following shortcomings: (1) needing subject-matter expertise (SME) to match the artificial intelligence (AI) model predictions to the gold standard, commonly referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, (3) automated validation of prediction missing context, and (4) auto-labeling causing inaccurate test accuracy. The method consists of (1) conducting contextual analysis, (2) assessing human cognitive workload, (3) determining decision points for applying artificial intelligence (AI), (4) defining the scope of the data, or annotated corpus required for training and validation of the cognitive services, (5) identifying and standardizing PV knowledge elements, (6) developing cognitive services, and (7) reviewing and validating cognitive services.
RESULTS: By applying the framework, we (1) identified 51 decision points as candidates for AI use, (2) standardized the process to make PV knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and context, (4) standardized acceptability by using established quality inspection principles, and (5) validated a total of 126 cognitive services.
CONCLUSION: The value of using AI methodologies in PV is compelling; however, as PV is highly regulated, acceptability will require assurances of quality, consistency, and standardization. We are proposing a foundational framework that the industry can use to identify and validate services to better support the gathering of quality data and to better serve the PV professional.

PMID: 31933254 [PubMed - in process]

An investigation into the avoidability of adverse drug reactions using the LAAT and modified Hallas tools.

Medicine (Baltimore). 2020 Jan;99(1):e18569

Authors: Danjuma MI, Shokri SA, Abubeker IY, Malik AE, Abdallah IMH, Shafei MNE, Fatima H, Mahmoud M, Hussain T, Maghoub Y, Sajid J, Zouki ANE

Abstract
An adverse drug reactions avoidability tool called the Liverpool ADR avoidability assessment tool (LAAT) was recently developed (for research purposes), and subsequently validated with mixed interrater reliability (IRR). We investigated the comparative IRR of this tool in an inpatient cohort to ascertain its practical application in this setting.The patient population was comprised of 44 ADR drug pairs drawn from an observational prospective cohort of patents with ADR attending a Weill Cornell Medicine-affiliated tertiary medical Centre in Doha Qatar (Hamad General Hospital). Using the LAAT, and modified Hallas tools, 4 independent raters (2 Clinical Pharmacologists, and 2 General Physicians) assessed and scored the 44 ADR-drug pairs. Agreement proportions between the rating pairs were evaluated as well individual/overall kappa statistics and intraclass correlation coefficients. We evaluated the weight of each of the 7 questions on the LAAT tool to ascertain its determinative role.Across 44 ADR-drug pairs, the overall median Fleiss kappa using the LAAT, and modified Hallas tools were 0.67 (interquartile range (IQR) 0.55, 0.76), 0.36 (IQR, 0.23-0.71) respectively. The overall percentage pairwise agreement with the LAAT and modified Hallas tools were 78.5%, and 62.2% respectively. Exact pairwise agreement occurred in 37 out of 44 (range 0.71-1), and 27 of 44 (0.53-0.77) ADR-drug pairs using the LAAT and modified Hallas tools respectively. Using the LAAT tool, the overall intraclass correlation coefficient was 0.68 (CI 0.55, 0.79), and 0.37 (CI 0.22, 0.53) with the modified Hallas tool.We report a higher proportion of "possible" and "definite" avoidability outcomes of adverse drug reactions compared with the modified Hallas, or that reported by developers of the LAAT tool. Although initially developed for research purposes, our report has suggested for the first time a potential applicability of this tool in clinical environment as well.

PMID: 31895800 [PubMed - indexed for MEDLINE]

Beyond Pegylated Interferon-Alpha: New Treatments for Hepatitis Delta.

AIDS Rev. 2019;21(3):126-134

Authors: Deterding K, Wedemeyer H

Abstract
Persistent coinfection with the hepatitis B/D viruses (HDV) represents the most severe form of viral hepatitis. Hepatitis D often leads to liver cirrhosis, hepatic decompensation, and hepatocellular carcinoma. The current treatment options are limited as only pegylated interferon-alpha (PEG-IFNa) has efficacy against HDV. However, treatment response is still unsatisfactory with 25-40% HDV RNA suppression after 1-2 years. In addition, late HDV RNA relapses have been described during long-term follow-up. Fortunately, new treatment options for patients with chronic hepatitis delta are now on the horizon. The hepatocyte entry inhibitor bulevirtide (formerly myrcludex B) and the farnesyl transferase inhibitor lonafarnib are currently explored in patients with chronic hepatitis delta in Phase 3 clinical studies. The nucleic acid inhibitor REP-2139-Ca and PEG-IFN-lambda are studied in Phase 2 trials. We here summarize data on the efficacy of these new antiviral drugs and the existing safety data on the treatment of HDV infection.

PMID: 31532397 [PubMed - indexed for MEDLINE]

Molecular Docking: Shifting Paradigms in Drug Discovery.

Int J Mol Sci. 2019 Sep 04;20(18):

Authors: Pinzi L, Rastelli G

Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

PMID: 31487867 [PubMed - indexed for MEDLINE]