Abstract: Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Abstract: Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug-drug, food-drug, and supplement-drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.

Abstract: The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Abstract: Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.

Abstract: The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

Abstract: Importance: Germany's unique approach to coverage determination and pricing has ensured that effective medicines remain on the market, often at prices reduced through negotiation. However, less is known about trade-offs of this approach with regard to initial availability of medicines. Objective: To examine differences in the timing and scope of new medicines available in Germany and the US. Design, Setting, and Participants: This retrospective cohort study analyzed initial availability of new medicines approved by regulatory agencies in Germany and the US between January 1, 2004, and December 31, 2018, and followed up through December 31, 2019. Data analysis was conducted from January 1, 2020, to July 1, 2022. A total of 599 novel approvals were reviewed. Generic, biosimilar, vaccine, and combination medicines were excluded. Exposures: US Food and Drug Administration approvals were reviewed for therapies categorized as new molecular entities or new active ingredients. German approvals were reviewed from secondary administrative data of authorized medicines that determine availability in Germany, including data presented by the European Medicines Agency. Main Outcomes and Measures: Approvals were analyzed to determine the percentage of medicines approved and available in the US, Germany, or both countries and compare the times to reach the market. Results: Analysis of 599 new medicines demonstrated that fewer were available in Germany compared with the US (80% vs 92% of all potential therapies) and that the median difference in time to market was 4 months (95% CI, -44.40 to 44.76 months). Forty-nine medicines were approved in Germany but not in the US, 75% of which were rejected by the US Food and Drug Administration, were withdrawn, or had US equivalent agents. Conclusions and Relevance: In this cohort study, fewer new medicines were available in Germany compared with the US between 2004 and 2018. In addition, drugs entered the German market later than in the US.

Abstract: The human peptide transporter hPepT1 (SLC15A1), physiologically transporting dipeptides and tripeptides generated during food digestion, also plays a role in the uptake of small bioactive peptides and peptide-like drugs. Moreover, it might be addressed in prodrug strategies of poorly absorbed drugs. We hypothesised that the cyclic drug peptides octreotide and pasireotide could be substrates of this transporter because their diameter can resemble the size of dipeptides or tripeptides due to their strong structural curvature and because they reach the systemic circulation in Beagle dogs. For investigating possible hPepT1 substrate characteristics, we generated and characterised a CHO-K1 cell line overexpressing SLC15A1 by transfection and selection via magnetic beads. Possible inhibition of the uptake of the prototypical substrate Gly-Sar by octreotide and pasireotide was screened, followed by quantifying the uptake of the cyclic peptides in cells overexpressing SLC15A1 compared with the parental cell line. Although inhibition of Gly-Sar uptake was observed, uptake of octreotide and pasireotide was not increased in SLC15A1 overexpressing cells, indicating a lack of transport by hPepT1. Our data clearly indicate that octreotide and pasireotide are nonsubstrate inhibitors of hPepT1 and that their oral bioavailability cannot be explained by absorption via hPepT1.

Abstract: BACKGROUND: Very little knowledge exists on the impact of Alzheimer's disease on the CNS target site pharmacokinetics (PK). AIM: To predict the CNS PK of cognitively healthy young and elderly and of Alzheimer's patients using the physiologically based LeiCNS-PK3.0 model. METHODS: LeiCNS-PK3.0 was used to predict the PK profiles in brain extracellular (brainECF) and intracellular (brainICF) fluids and cerebrospinal fluid of the subarachnoid space (CSFSAS) of donepezil, galantamine, memantine, rivastigmine, and semagacestat in young, elderly, and Alzheimer's patients. The physiological parameters of LeiCNS-PK3.0 were adapted for aging and Alzheimer's based on an extensive literature search. The CNS PK profiles at plateau for clinical dose regimens were related to in vitro IC50 values of acetylcholinesterase, butyrylcholinesterase, N-methyl-D-aspartate, or gamma-secretase. RESULTS: The PK profiles of all drugs differed between the CNS compartments regarding plateau levels and fluctuation. BrainECF, brainICF and CSFSAS PK profile relationships were different between the drugs. Aging and Alzheimer's had little to no impact on CNS PK. Rivastigmine acetylcholinesterase IC50 values were not reached. Semagacestat brain PK plateau levels were below the IC50 of gamma-secretase for half of the interdose interval, unlike CSFSAS PK profiles that were consistently above IC50. CONCLUSION: This study provides insights into the relations between CNS compartments PK profiles, including target sites. CSFSAS PK appears to be an unreliable predictor of brain PK. Also, despite extensive changes in blood-brain barrier and brain properties in Alzheimer's, this study shows that the impact of aging and Alzheimer's pathology on CNS distribution of the five drugs is insignificant.

Abstract: The first cases of the novel coronavirus, SARS-CoV-2, were detected in December 2019 in Wuhan, China. Nucleotide substitutions and mutations in the SARS-CoV-2 sequence can result in the evolution of the virus and its rapid spread across the world. Therefore, understanding genetic variants of SARS-CoV-2 and targeting the conserved elements responsible for viral replication have great benefits for detecting its infection sources and diagnosing and treating COVID-19. In this study, we used the SARS-CoV-2 sequence isolated from a 59-year-old man in Ardabil, Iran, in April 2020 and sequenced using Oxford Nanopore technology. A meta-analysis comparing the sequence under study with other sequences from Iran indicated long nucleotide insertions/deletions (indels) that code for NSP15, the NSP14-NSP10 complex, open reading frame ORF9b, and ORF1ab polyproteins. In addition, replicating the NSP8 protein in the study sequence is another topic that can affect viral replication. Then using the DNA structure of NSP8, NSP15, NSP14-NSP10 complex, and ORF1ab as a genetic target can help find drug-like compounds for COVID-19. Potential drug-like compounds reported in this study for their mechanism of action and interactions with SARS-CoV-2 genes using drug repurposing are resveratrol, erythromycin, chloramphenicol, indomethacin, ciclesonide, and PDE4 inhibitor. Ciclesonide appears to show the best results when docked with chosen viral proteins. Therefore, different proteins isolated from nucleotide mutations in the virus sequence can indicate distinct inducers for antibodies and are important in vaccine design.

Abstract: Objective: Available treatments of depression have limited efficacy and unsatisfactory remission rates. This study aims to review randomized controlled trials (RCTs) investigating effects of glutamate receptor modulators in treating patients with resistant depression. Method : The study protocol was registered in PROSPERO (CRD42021225516). Scopus, ISI Web of Science, Embase, Cochrane Library, Google Scholar, and three trial registries were searched up to September 2020 to find RCTs evaluating glutamate receptor modulators for resistant depression. The difference between intervention and control groups in changing depression scores from baseline to endpoint was considered the primary outcome. Version 2 of the Cochrane risk-of-bias tool for randomized trials was used to assess the quality of the RCTs. No funding was received. Results: Thirty-eight RCTs were included. Based on the included studies, compelling evidence was found for ketamine (with or without electroconvulsive therapy, intravenous or other forms), nitrous oxide, amantadine, and rislenemdaz (MK-0657); the results for MK-0657, amantadine, and nitrous oxide were only based on one study for each. Lithium, lanicemine, D-cycloserine, and decoglurant showed mixed results for efficacy, and, riluzole, and 7-chlorokynurenic acid were mostly comparable to placebo. A limited number of studies were available that addressed drugs other than ketamine. Conclusion: The study cannot determine the difference between statistical and clinical significance between the agents and placebo due to high heterogeneity among the RCTs. Nevertheless, ketamine could be used as an efficacious drug in TRD; still, additional studies are needed to delineate the optimum dosage, duration of efficacy, and intervals. Further studies are also recommended on the effectiveness of glutamatergic system modulators other than ketamine on treatment-resistant depression.