Drug Database Research Articles

National Center for Tumor Diseases Precision Oncology Thesaurus for Drugs: A Curated Database for Drugs, Drug Classes, and Drug Targets in Precision Cancer Medicine.

National Center for Tumor Diseases Precision Oncology Thesaurus for Drugs: A Curated Database for Drugs, Drug Classes, and Drug Targets in Precision Cancer Medicine.

Mitigating the Risk of Drug Interactions in Cancer Patients Taking Oral Anticancer Agents: The Role of a Multidisciplinary Team-Based Medication Reconciliation.

Polypharmacy in cancer patients is a recognized issue and should be an integral part of comprehensive patient assessment and management. Despite this, a systematic review of concomitant drugs or a search for potential drug-drug interactions (DDIs) is not always performed. Here, we present the results of a medication reconciliation model performed by a multidisciplinary team to identify clinically meaningful potential DDIs (defined by the presence of DDIof major severity or contraindication) in cancer patients undergoing oral antineoplastic drugs. From June to December 2022, we performed a non-interventional, prospective, cross-sectional, single-center study of adult cancer patients, initiating or undergoing treatment with oral antineoplastic drugs, referred by their oncologists for therapeutic review regarding potential DDIs. DDIs were assessed by a multidisciplinary team of hospital pharmacists and medical oncologists, through research in three different drug databases as well as in the summary of product characteristics. A report detailing all potential DDIs was created for each request and provided to the patient's medical oncologist for further examination. Overall, 142 patients' medications were reviewed. Regardless of the severity or clinical importance, 70.4% of patients had at least one potential DDI. We found 184 combinations of oral anticancer and regular therapy agents with potential DDIs, 55 of whom were considered of major severity by at least one DDI database. As expected, the number of potential DDIs increased with the number of active substances in regular therapy (p <0.001), but we did not find an increased relation between age and the total number of potential DDIs (p =0.109). Thirty-nine (27.5%) patients had at least one clinically meaningful DDI identified. After adjustment through multivariable logistic regression, only the female sex (odds ratio (OR) 3.01,p= 0.029), the number of active comorbidities (OR 0.60,p= 0.029), and the presence of proton pump inhibitors in chronic medication (OR 2.99,p= 0.033) remained as predictors of potential meaningful DDI. Although drug interactions are a concern in oncology, a systematic DDI review is rarely conducted in medical oncology consultations. The availability of a medication reconciliation service, carried out by a multidisciplinary team with dedicated time for this task, is an added value for safety enhancement in cancer patients.

Comprehensive guidance for antibiotic dosing in obese adults: 2022 update.

Drug dosing in obese patients continues to be challenging due to a lack of high-quality evidence to guide dosing recommendations. We first published guidance for antibiotic dosing in obese adults in 2017, in which we critically reviewed articles identified from a broad search strategy to develop dosing recommendations for 35 antimicrobials. In this updated narrative review, we searched Pubmed, Web of Science, and the Cochrane Library using Medical Subject Headings including anti-infectives, specific generic antimicrobial names, obese, pharmacokinetics, and others. We reviewed 393 articles, cross-referenced select cited references, and when applicable, referenced drug databases, package inserts, and clinical trial data to update dosing recommendations for 41 antimicrobials. Most included articles were pharmacokinetic studies, other less frequently included articles were clinical studies (mostly small, retrospective), case reports, and very rarely, guidelines. Pharmacokinetic changes are frequently reported, can be variable, and sometimes conflicting in this population, and do not always translate to a documented difference in clinical outcomes, yet are used to inform dosing strategies. Extended infusions, high doses, and therapeutic drug monitoring remain important strategies to optimize dosing in this population. Additional studies are needed to clinically validate proposed dosing strategies, clarify optimal body size descriptors, dosing weight scalars, and estimation method of renal function in obese patients.

Computer-assisted discovery of safe and effective DprE1/ aaRSs Inhibitors against TB utilizing Drug Repurposing approach

BackgroundThe emergence of various drug-resistant strains of Mycobacterium tuberculosis compelled medicinal chemists to expedite the discovery of novel, safer alternatives to present regimens. Decaprenylphosphoryl-β-d-ribose 2′-epimerase (DprE1), an essential component of arabinogalactan biosynthesis, has been considered a novel target for developing new inhibitors against Tuberculosis. We aimed to discover DprE1 inhibitors utilizing the drug repurposing approach. MethodsA structure-based virtual screening of FDA and world-approved drugs database was carried out, and initially, 30 molecules were selected based on their binding affinity. These compounds were further analyzed by molecular docking with extra-precision mode, MMGBSA binding free energy estimation, and prediction of ADMET profile. ResultsBased on the docking results and MMGBSA energy values- ZINC000006716957, ZINC000011677911, and ZINC000022448696 were identified to be the top three hit molecules with good binding interactions inside the active site of DprE1. These hit molecules were subjected to molecular dynamics (MD) simulation for a period of 100 ns to study the dynamic nature of the binding complex. MD results were in accordance with molecular docking and MMGBSA analysis showing protein-ligand contacts with key amino acid residues of DprE1. ConclusionBased on their stability throughout the 100 ns simulation, ZINC000011677911 was the best in silico hit with an already known safety profile. This molecule could lead to future optimization and development of new DprE1 inhibitors.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Bioinformatic approach for repurposing immunomodulatory drugs for lepromatous leprosy.

The lepromatous leprosy (LL) disease is caused by Mycobacterium leprae and Mycobacterium lepromatosis which is characterized by inadequate response to treatment, a propensity to drug resistance, and patient disability. We aimed to evaluate current immunomodulatory medicines and their target proteins collectively as a drug repurposing strategy to decipher novel uses for LL. A dataset of human genes associated with LL-immune response was retrieved from public health genomic databases including the Human Genome Epidemiology Navigator and DisGeNET. Retrieved genes were filtered and enriched to set a robust network (≥10, up to 21 edges) and analyzed in the Cytoscape program (v3.9). Drug associations were obtained in the NDEx Integrated Query (v1.3.1) coupled with drug databases such as ChEMBL, BioGRID, and DrugBank. These networks were analyzed in Cytoscape with the CyNDEx-2 plugin and STRING protein network database. Pathways analyses resulted in 100 candidate drugs organized into pharmacological groups with similar targets and filtered on 54 different drugs. Gene-target network analysis showed that the main druggable targets associated with LL were tumoral necrosis factor-alpha, interleukin-1B, and interferon-gamma. Consistently, glucosamine, binimetinib, talmapimod, dilmapimod, andrographolide, and VX-702 might have a possible beneficial effect coupled with LL treatment. Based on our drug repurposing analysis, immunomodulatory drugs might have a promising potential to be explored further as therapeutic options or to alleviate symptoms in LL patients.

Primary care of Ukrainian refugees

Since the beginning of the war in Ukraine, more than 7.6million people from Ukraine have been registered as refugees in European countries. In Germany, the number is estimated to be more than 1million. These refugees may have different health needs than German patients due to differences in the health care system, disease prevalence, preventive measures, health behavior, and experiences of flight. However, general practitioners (GPs) have hardly been prepared for the contact. For the first time, challenges in the treatment of Ukrainian refugees and support needs of German GPs were determined. In July and August 2022, across-sectional study among GPs in Germany was conducted using an online survey. A total of 82GPs participated with aresponse rate of 16.0%; 52of the participating GPs had treated Ukrainian refugees in the previous 2weeks. In all, 75.0% of them reported difficulties or peculiarities in care, especially in communication (61.5%), due to lack of information about previous illnesses (34.6%), and expectations of services to be provided (30.8%). Of the 82participants, 59.8% reported aneed for multilingual information for patients, especially about the German health care system, help with mental health problems, contact points, and differences in the use of medications. Information for the practice team is needed in 37.8% of cases, especially on possibilities in case of language barriers, vaccination coverage in Ukraine, and dealing with missing vaccination records as well as drug lists. Due to the new situation of Ukrainian refugees in Germany and the mentioned barriers, GPs should be supported in care. Information for practice teams as well as their networking with psychotherapeutic offers, contact points, drug databases, and regional interpreter services are urgently needed. However, multilingual information for Ukrainian patients should be disseminated in order to relieve the burden on practices, which have been under great strain, and to ensure continuity and quality of care.

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

Assessment of Xenoestrogens in Jordanian Water System: Activity and Identification

Sex hormone disruptors (xenoestrogens) are a global concern due to their potential toxicity. However, to date, there has been no study to investigate the presence of xenoestrogen pollutants in the Jordanian water system. Samples in triplicates were collected from six locations in Jordan, including dams, surface water, tap or faucet water, and filtered water (drinking water-local company). Xenoestrogens were then extracted and evaluated with a yeast estrogen screen utilizing Saccharomyces cerevisiae. Later, possible pollutants were mined using ultrahigh-performance liquid chromatography (UPLC) coupled with a Bruker impact II Q-TOF-MS. Possible hits were identified using MetaboScape software (4000 compounds), which includes pesticide, pharmaceutical pollutant, veterinary drug, and toxic compound databases and a special library of 75 possible xenoestrogens. The presence of xenoestrogens in vegetable samples collected from two different locations was also investigated. The total estrogen equivalents according to the YES system were 2.9 ± 1.2, 9.5 ± 5, 2.5 ± 1.5, 1.4 ± 0.9 ng/L for King Talal Dam, As-Samra Wastewater Treatment Plant, King Abdullah Canal, and tap water, respectively. In Almujeb Dam and drinking water, the estrogenic activity was below the detection limit. Numbers of identified xenoestrogens were: As-Samra Wastewater Treatment Plant 27 pollutants, King Talal Dam 20 pollutants, Almujeb Dam 10 pollutants, King Abdullah Canal 16 pollutants, Irbid tap water 32 pollutants, Amman tap water 30 pollutants, drinking water 3 pollutants, and vegetables 7 pollutants. However, a large number of compounds remained unknown. Xenoestrogen pollutants were detected in all tested samples, but the total estrogenic capacities were within the acceptable range. The major source of xenoestrogen pollutants was agricultural resources. Risk evaluations for low xenoestrogen activity should be taken into account, and thorough pesticide monitoring systems and regular inspections should also be established.

X-ray Diffraction study for chemical analysis of Rajat based bhasmas

X-ray diffraction analysis of the Rajat based selected bhasmas was one of the advanced parameter for detailed scientific scrutiny in this study. Ayurvedic literature is enriched with various techniques for establishing dimensions to quality control for achieving standard acceptable form of bhasma. X-ray diffraction is modern and sophisticated method which provides relevant chemical database of the herbo-metallic drugs. Quality of bhasma determines on the basis of chemical composition, processing and applications, so it can be used safely. X-ray diffraction pattern data detects size, physical state and surface properties. For Rajat based selected bhasmas, reduced particle size was observed in this study which will be useful to determine extent of therapeutic effectiveness of particular bhasma as per amount of quantity and quality in each dose. Rajat based selected bhasmas are herbo-mineral complex compounds which are recommended for treatment of several ailments from reputed pharmacies. Systematic pathways for the synthesis of ayurvedic bhasmas from heavy metals include Lead, Iron, Gold, Copper, Zinc, Silver, Mercury and Arsenic. The potential of ayurvedic literature and drugs need to be validated by assessing quality and purity on the basis of modern medicine protocol for treatment. During drug delivery, absorption as well as assimilation capacity enhances with particle size of drugs in the body. Hence selection of drug associated with physical parameter for best result as per prescribed doses in treatment. Bhasmikarana is the significant step in the preparation of bhasma. The idea underlying the process of “bhasmikarana” is to convert the substance into ash having the resultant properties in an active state. The bhasma not only radically remove the root cause of the disease, but also enhances the immune system inherent in the human body. Now market is having a wide range of bhasmas, as required to be processed according to unique, standard and advanced ayurvedic method.

Investigation of the Effects of Glabridin on the Proliferation, Apoptosis, and Migration of the Human Colon Cancer Cell Lines SW480 and SW620 and Its Mechanism Based on Reverse Virtual Screening and Proteomics.

Colon cancer is a relatively common malignant tumor of the digestive tract. Currently, most colon cancers originate from adenoma carcinogenesis. By screening various licorice flavonoids with anticancer effects, we found that glabridin (GBN) has a prominent anticolon cancer effect. First, we initially explored whether GBN can inhibit proliferation, migration, and invasion and induce apoptosis in SW480 and SW620 cells. Next, we exploited reverse virtual and proteomics technologies to screen out closely related target pathways on the basis of a drug and target database. At the same time, we constructed the structure of the GBN target pathway in colon cancer. We predicted that GBN can regulate the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT)-mammalian target of the rapamycin pathway (mTOR) pathway to fight colon cancer. Finally, through Western blot analysis and qRT-PCR, we verified that the expression levels of the PI3K, AKT, and mTOR proteins and genes in this pathway were significantly reduced after GBN administration. In short, the promising discovery of the anticolon cancer mechanism of GBN provides a reliable experimental basis for subsequent new drug development.

Zhijing powder manages blood pressure by regulating PI3K/AKT signal pathway in hypertensive rats

BackgroundZhijing Powder (ZJP) is a traditional Chinese medicine containing two kinds of Chinese medicine. Those studies analyze the molecular mechanism of ZJP in treating hypertension through network pharmacology, combined with animal experiments. MethodsFirst, the effective ingredients and potential targets of the drug were obtained through drug databases, while the targets of disease obtained through disease target databases. The potential targets, cellular bioanalysis and signaling pathways were found in some platforms by analyzing collected targets. Further experiments were conducted to verify the effect and mechanism of drugs on cold and high salt in an induced-hypertension rat model. ResultsThere are 17 effective components of centipedes and 10 of scorpions, with 464 drug targets obtained after screening. A total of 1263 hypertension targets were obtained after screening and integration, resulting in a protein-protein interaction network (PPI) with 145 points and 1310 edges. Gene ontology (GO) analysis shows that blood circulation regulation and activation of G protein-coupled receptors are mainly biological processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis shows that neuroactive ligand-receptor interaction, calcium signaling pathways, PI3K-AKT signaling pathways are the most abundant gene-enriched pathway. Animal experiments indicated that ZJP can reduce blood pressure (BP), affect expression of the PI3K-AKT signaling pathway, and improve oxidative stress in the body. ConclusionZJP ameliorates oxidative stress and reduces BP in hypertensive rats caused by cold stimuli and high salt, revealing its effect on the expression of the PI3K/AKT signaling pathway in the rat aorta.

Molecular Docking and Simulation Studies of Flavanone and its Derived Compounds on PI3K-AKT Pathway Targeting against Cancer.

Flavanone compounds and their related derivatives are reported in controlling cell cycle, angiogenesis, and metastasis. Phosphoinositide 3-kinases is a major drug target. Crystalize structure of Phosphoinositide 3-kinases-Akt complex obtained from Protein Data Bank (PDBID: 3CQW) was selected as receptor protein and the binding site has been identified with PDBSum Database. Flavanone and its derivatives were retrieved using freely available existing drug databases like Drug Bank, Zinc, and PubChem. New derivatives were modified by altering the flavanone at Beta ring position. This modification would help in maintaining stable structural conformation and retaining better anticancer activity. Retrieved Flavanone derivatives from the drug database were docked against 3CQW Protein with the advanced docking tool FlexX. MD simulations of the best molecule were performed with the Desmond package by calculating nonbonding interactions such as electrostatic interaction and hydrogen bond stable and favorable conformations were calculated. These interaction studies would help identify new potential drug candidates with the help of computer-aided drug designing techniques. Natural chemicals have received a lot of attention because of their vast range of applications in human health and disease prevention without creating any negative side effects. Molecular docking is an essential approach for drug development since it allows for effective screening of potential therapeutics in a short time. We hypothesized in this paper that natural flavanone and its derivatives may be effective as Akt-1 inhibitors.

Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

Nowadays, smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature, computational approaches, and discoveries, owing to which a massive quantity of experimental datasets was published and generated (Big Data) for describing and validating such novelties. Drug-drug interaction (DDI) significantly contributed to drug administration and development. It continues as the main obstacle in offering inexpensive and safe healthcare. It normally happens for patients with extensive medication, leading them to take many drugs simultaneously. DDI may cause side effects, either mild or severe health problems. This reduced victims’ quality of life and increased hospital healthcare expenses by increasing their recovery time. Several efforts were made to formulate new methods for DDI prediction to overcome this issue. In this aspect, this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction (SHODL-DDIP) model in a big data environment. In the presented SHODL-DDIP technique, the relativity and characteristics of the drugs can be identified from different sources for prediction. The input data is preprocessed at the primary level to improve its quality. Next, the salp swarm optimization algorithm (SSO) is used to select features. In this study, the deep belief network (DBN) model is exploited to predict the DDI accurately. The SHO algorithm is involved in improvising the DBN model’s predictive outcomes, showing the novelty of the work. The experimental result analysis of the SHODL-DDIP technique is tested using drug databases, and the results signified the improvements of the SHODL-DDIP technique over other recent models in terms of different performance measures.

Therapy of women with multiple sclerosis: an analysis of the use of drugs that may have adverse effects on the unborn child in the event of (unplanned) pregnancy

Although effective contraception is strongly recommended during the therapy of women with multiple sclerosis (MS) with some immunomodulatory drugs, unplanned pregnancies still occur. Adequate medication management is essential to avoid foetal harm in the event of an unplanned pregnancy. The aim was to screen for medications used in women of childbearing age with MS that may pose a risk of side effects on foetal development. Sociodemographic, clinical and medication data were collected from 212 women with MS by structured interviews, clinical examinations and medical records. Using the databases from Embryotox, Reprotox, the Therapeutic Goods Administration and on the German summaries of product characteristics, we assessed whether the taken drugs were potentially harmful regarding the foetal development. The majority of patients (93.4%) were taking one or more drugs for which a possible harmful effect on the foetus is indicated in at least one of the four databases used. This proportion was even higher in patients who used hormonal contraceptives (birth control pills or vaginal rings) (PwCo, n = 101), but it was also quite high in patients who did not use such contraceptives (Pw/oCo, n = 111) (98.0% and 89.2%, respectively). PwCo were significantly more likely to take five or more medications with potential foetal risk according to at least one database than Pw/oCo (31.7% versus 6.3%). PwCo were also more severely disabled (average Expanded Disability Status Scale score: 2.8 versus 2.3) and more frequently had comorbidities (68.3% versus 54.1%) than Pw/oCo. Data on the most commonly used drugs in MS therapy were gathered to study the risk of possible drug effects on foetal development in female MS patients of childbearing age. We found that the majority of drugs used by patients with MS are rated as having a potential risk of interfering with normal foetal development. More effective contraception and special pregnancy information programmes regarding the therapy management during pregnancy should be implemented to reduce potential risks to mother and child. Use of drugs not recommended during pregnancy by women with multiple sclerosis Introduction: Patients with multiple sclerosis (MS) often have to take different drugs simultaneously. During the therapy with some immunomodulatory drugs, effective contraception is strongly recommended. Nevertheless, unplanned pregnancies occur regularly in women with MS.Methods: Here, we investigated whether the 212 patients included in this study were taking drugs with known possibility of harm to the development of an unborn child. This was done using four different drug databases.Results: A subset of 111 patients was not taking hormonal contraceptives (birth control pills or vaginal rings). Of those, 99 patients were taking at least one drug that is not recommended during pregnancy according to at least one of the four databases. Most of the medications taken have the potential to affect normal foetal development.Conclusion: To ensure safe use of medications, the patients should be reminded of the importance of effective contraception.

Elevated ADAM-like Decysin-1 (ADAMDEC1) expression is associated with increased chemo-sensitivity and improved prognosis in breast cancer patients.

Chemoresistance is problematic and its mechanisms are unclear in breast cancer. More predictive markers are urgently required. GSE32646, GSE34138, and GSE20271 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between chemosensitive and chemoresistant tumors. LinkedOmics was used to analyze ADAM-like Decysin-1 (ADAMDEC1) expression among patients with different clinical characteristics and detect co-expression genes for functional analysis. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to investigate the association between the target gene and the immune response. Gene Set Cancer Analysis (GSCA) was utilized to explore the related pathways of ADAMDEC1 and evaluate the correlation between the expression of ADAMDEC1 and drug sensitivity. RNA22, miRWalk, and miRmap were used to predict the upstream micro ribonucleic acids (miRNAs) regulating ADAMDEC1 expression, while DIANA-LncBase v2 was applied to predict the upstream long non-coding ribonucleic acids (lncRNAs). Kaplan-Meier curve analysis was applied to determine the survival time. We identified that ADAMDEC1 was upregulated among chemosensitive triple-negative breast cancer (TNBC) tissues in GSE32646, GSE34138, and GSE20271. Higher expression of ADAMDEC1 indicated longer survival in breast cancer. Next, we found that ADAMDEC1 was significantly related to the immune response in breast cancer through functional enrichment analysis and further meta-data validation. Moreover, we recognized that hsa-miR-4534 was the potential upstream miRNA regulating ADAMDEC1 expression and Taurine Up-Regulated 1 (TUG1) was the most likely upstream lncRNA of ADAMDEC1 and hsa-miR-4534. Finally, the correlation between ADAMDEC1 and chemosensitivity was confirmed through drug database analysis. Elevated ADAMDEC1 expression is associated with increased chemosensitivity and better prognosis in breast cancer patients.

Comprehensive Characterization of HATs and HDACs in Human Cancers Reveals Their Role in Immune Checkpoint Blockade.

Histone acetylation that controlled by two mutually antagonistic enzyme families, histone acetyl transferases (HATs) and histone deacetylases (HDACs), as one of major epigenetic mechanisms controls transcription and its abnormal regulation was implicated in various aspects of cancer. However, the comprehensive understanding of HDACs and HATs in cancer is still lacking. Systematically analysis through 33 cancer types based on next-generation sequence data reveals heterogeneous expression pattern of HDACs and HATs across different cancer types. In particular, HDAC10 and HDAC6 show significant downregulation in most cancers. Principal components analysis (PCA) of pan-cancer reveals significant difference of HDACs and HATs between normal tissues and normal tissue adjacent to the tumor. The abnormal expression of HDACs and HATs was partially due to CNV and DNA methylation in multiple types of cancer. Prognostic significance (AUC reached 0.736) of HDACs and HATs demonstrates a five-gene signature including KAT2A, HAT1, KAT5, CREBBP and SIRT1 in KIRC. Analysis of NCI-60 drug database reveals the cytotoxic effect of several drugs are associated with dysregulated expression of HDACs and HATs. Analysis of immune infiltration and immunotherapy reveals that KAT2B and HDAC9 are associated with immune infiltration and immunotherapy. Our analysis provided comprehensive understanding of the regulation and implication of HDACs and HATs in pan-cancer. These findings provide novel evidence for biological investigating potential individual HDACs and HATs in the development and therapy of cancer in the future.

Substance use before or during pregnancy and the risk of child mortality, perinatal morbidities and congenital anomalies.

We aimed to investigate child mortality, perinatal morbidities and congenital anomalies born by women with substance misuse during or before pregnancy (DP or BP). Taiwan Birth Registration from 2004 to 2014 linking Integrated Illicit Drug Databases used to include substance misuse participates. Children born by mothers convicted of substance misuse DP or BP were the substance-exposed cohort. Two substance-unexposed comparison cohorts were established: one comparison cohort selected newborns from the rest of the population on a ratio of 1:1 and exact matched by the child's gender, child's birth year, mother's birth year and child's first use of the health insurance card; another comparison cohort matched newborns from exposed and unexposed mothers by their propensity scores calculated from logistic regression. The exposure group included 1776 DP, 1776 BP and 3552 unexposed individuals in exact-matched cohorts. A fourfold increased risk of deaths in children born by mothers exposed to substance during pregnancy was found compared to unexposed group (hazard ratio [HR] = 4.54, 95% confidence interval (CI): 2.07-9.97]. Further multivariate Cox regression models with adjustments and propensity matching substantially attenuated HRs on mortality in the substance-exposed cohort (aHR = 1.62, 95% CI: 1.10-2.39). Raised risks of perinatal morbidities and congenital anomalies were also found. Increased risks of child mortality, perinatal morbidities or congenital anomalies were found in women with substance use during pregnancy. From estimates before and after adjustments, our results showed that having outpatient visits or medical utilizations during pregnancy were associated with substantially attenuated HRs on mortality in the substance-exposed cohort. Therefore, the excess mortality risk might be partially explained by the lack of relevant antenatal clinical care. Our finding may suggest that the importance of early identification, specific abstinence program and access to appropriate antenatal care might be helpful in reducing newborn mortality. Adequate prevention policies may be formulated.

Improving Drug Combination Repositioning using Positive Unlabelled Learning and Ensemble Learning

Drug repositioning is a cost-effective and time-effective concept that enables the use of existing drugs/drug combinations for therapeutic effects. The number of drug combinations used for therapeutic effects is smaller than all possible drug combinations in the present drug databases. These databases consist of a smaller set of labelled positives and a majority of unlabelled drug combinations. Therefore, there is a need for determining both reliable positive and reliable negative samples to develop binary classification models. Since, we only have labelled positives, the unlabelled data has to be separated into positives and negatives by a reliable technique. This study proposes and demonstrates the significance of using Positive Unlabelled Learning, for determining reliable positive and negative drug combinations for drug repositioning. In the proposed approach, the dataset with known positives and unlabelled samples was clustered by a Deep Learning based Self Organizing Map. Then, an ensemble learning methodology was followed by employing three classification models. The proposed PUL model was compared with the frequently used approach that randomly selects negative drug pairs from unlabelled samples. A significant improvement of 19.15%, 20.56% and 20.23% in the Precision, Recall and F-measure, respectively, was observed for the proposed PUL-based ensemble learning approach. Moreover, 128 drug repositioning candidates were predicted by the proposed methodology. Further, we found literature-based evidence to support five drug combinations that may be able to be repositioned. These discoveries show our proposed PUL approach as a promising strategy that is applicable in drug combination prediction for repositioning.

Dimensional Reduction of QSAR Features Using a Machine Learning Approach on the SARS-Cov-2 Inhibitor Database

Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical composition of a molecule to its biochemical, pharmaceutical and biological activities. The characteristics of a molecule's chemical constituents, such as chemical descriptors and fingerprints, are necessary to create a good QSAR model. Dimensionality reduction can alleviate the issue of several unnecessary and redundant chemical descriptors and chemical fingerprints in a high-dimensional feature-number data set by shrinking the high-dimensional original space to a low-dimensional intrinsic space. There are two categories of dimensional reduction techniques: feature extraction and feature selection. The dimension reduction approach can be utilized as a starting step in running a QSAR Virtual Screening Model on a dataset of SARS-CoV-2 inhibitor medications to create novel treatments for Covid-19 cases based on machine learning (ML) and the idea of medicinal repurposing. Fe extraction and feature selection are crucial to determining which feature sets should be applied to a specific classification process in QSAR modeling to produce reliable virtual screening results. The SARS-Cov-2 inhibitor drug database's chemical descriptor and chemical fingerprint were extracted using a simple, quick, and accurate method in this work. The total number of selected features is 12122 features. PCA, Missing values, and Random Forest are the techniques employed. The Xgboost Tree Ensemble, Naive Bayes, Support Vector Machine, Random Forest, and Deep Learning (Artificial Neural Network/Multilayer Perceptron) were used to classify the QSAR modeling on the training and test data. The Random Forest approach, when applied to all chemical descriptors and chemical fingerprint features, along with the XGBoost algorithm, yields the best feature selection results (accuracy value of 0.845 and AUC of 0.904). There are 233 characteristics for the regression QSAR approach and 273 features for the feature selection-based QSAR method of classification. Next, virtual screening of QSAR modeling of prospective drugs for Covid-19 therapy can be done utilizing the outcomes of the characteristics that have been chosen using the Random Forest approach