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Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Background: Non-specific treatment effects, such as expectations, contribute to the effectiveness of pharmacological treatments across diseases. However, the contribution of expectancy, i.e., certainty of receiving treatment, in patients with Alzheimer’s disease (AD) is unknown. Objective: The aim is to investigate whether certainty of receiving a genuine treatment influences the response to active treatment in AD patients. Methods: The efficacy of active treatments in open-label trials, where patients are certain of receiving treatment (100%certainty), was compared to the same active treatments in randomized controlled trials (RCT), where patients are uncertain of receiving treatment or placebo (50%certainty). Results: In the seven open-label trials, there was no significant difference between post- and pre-treatment scores (difference in means = 0.14, 95%CI [–0.51; 0.81], p = 0.66). In the eight RCT trials, there was a significant difference between post- and pre-treatment (difference in means = –0.91, 95%CI [–1.43; –0.41], p

Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address such data discrepancy in drug response prediction for different cancers. These methods generally use cell lines as source domains, and patients, patient-derived xenografts or other cell lines as target domains; however, it is assumed that the methods have access to the target domain during training or fine-tuning, and they can only take labelled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic, whereas the latter means these methods rely on labelled source domains that are of limited size. To avoid these assumptions, we formulate drug response prediction in cancer as an out-of-distribution generalization problem, which does not assume that the target domain is accessible during training. Moreover, to exploit unlabelled source domain data—which tends to be much more plentiful than labelled data—we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labelled and unlabelled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization and a consistency loss to incorporate unlabelled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts and patients. Finally, we showed that Velodrome models generalize to different tissue types that were well-represented, under-represented or completely absent in the training data. Overall, our results suggest that Velodrome may guide precision oncology more accurately. Providing patient specific predictions for drug responses is challenging as preclinical data across a large population is hard to collect. Sharifi-Noghabi and colleagues present a semi-supervised method to predict drug response from limited data that can generalize successfully to different tissue types.

The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18–21 and are established driver mutations in non-small cell lung cancer (NSCLC)1–3. Targeted therapies are approved for patients with ‘classical’ mutations and a small number of other mutations4–6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7–10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure–function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure–function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

BackgroundOne of the major challenges in precision medicine is accurate prediction of individual patient’s response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model.ResultsWe presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance.ConclusionWe implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets.

Although it has long been deemed "undruggable", with the development of drugs specifically binding the KRAS-G12C mutant protein, clinical trials that directly inhibit oncogenic RAS have recently made promising improvements. In particular, the covalent KRAS-G12C inhibitors sotorasib and adagrasib are used to treat patients with advanced non-small cell lung cancer (NSCLC) carrying KRAS-G12C mutations. Unfortunately, the vast majority of patients do not respond to KRAS-G12C inhibitor therapy, mainly due to intrinsic or acquired resistance caused by cellular, molecular, and genetic mechanisms. Improving the understanding of drug response in the tumor microenvironment may continue to promote the design, testing, and clinical application of KRAS-G12C inhibitors.

Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

In 2019, the Veterans Affairs (VA), the largest integrated US healthcare system, started the Pharmacogenomic Testing for Veterans (PHASER) clinical program that provides multi-gene pharmacogenomic (PGx) testing for up to 250,000 veterans at approximately 50 sites. PHASER is staggering program initiation at sites over a 5-year period from 2019 to 2023, as opposed to simultaneous initiation at all sites, to facilitate iterative program quality improvements through Plan-Do-Study-Act cycles. Current resources in the PGx field have not focused on multisite, remote implementation of panel-based PGx testing. In addition to bringing large scale PGx testing to veterans, the PHASER program is developing a roadmap to maximize uptake and optimize the use of PGx to improve drug response outcomes.

Drug-radiotherapy is a common and effective combinational treatment for cancer. This study aimed to explore the ionizing radiation-optimized drug treatment based on nanomaterials so as to improve the synergistic efficacy of drug-radiotherapy against cancer and limit the adverse effect on healthy organs. In this review, these emerging strategies were divided into four parts. First, the delivery of the drug-loaded nanoparticles was optimized owing to the strengthened passive targeting process, active targeting process, and cell targeting process of nanoparticles after ionizing radiation exposure. Second, nanomaterials were designed to respond to the ionizing radiation, thus leading to the release of the loading drugs controllably. Third, radiation-activated pro-drugs were loaded onto nanoparticles for radiation-triggered drug therapy. In particular, nontoxic nanoparticles with radiosensitization capability and innocuous radio-dynamic contrast agents can be considered as radiation-activated drugs, which were discussed in this review. Fourth, according to the various synergetic mechanisms, radiotherapy could improve the drug response of cancer, obtaining optimized drug-radiotherapy. Finally, relative suggestions were provided to further optimize these aforementioned strategies. Therefore, a novel topic was selected and the emerging strategies in this region were discussed, aiming to stimulate the inspiration for the development of ionizing radiation-optimized drug treatment based on nanomaterials.

Graphical abstract, Alterations in the spliceosome pathway (SP) have been associated with diverse human cancers. In this study, we explored associations of the SP activity with various clinical features, anti-tumor immune signatures, tumor immunity-related genomic and molecular features, and the response to immunotherapies and targeted therapies in 29 cancer types from The Cancer Genome Atlas (TCGA) database. We showed that the SP activity was an oncogenic signature, as evidenced by its hyperactivation in cancer and invasive cancer subtypes and correlations with unfavorable clinical outcomes and anti-tumor immunosuppression in various cancers. The SP activity showed positive correlations with tumor mutation burden (TMB) and aneuploidy in diverse cancers, suggesting its association with genomic instability. However, the negative association between the SP activity and anti-tumor immune response was independent of its associations with aneuploidy and TMB. Furthermore, we supported that the SP activity had a negative correlation with immunotherapy response in four cancer cohorts treated by immune checkpoint inhibitors. Moreover, elevated SP activity is correlated with increased drug sensitivity for a broad spectrum of anti-tumor targeted therapies. In conclusion, the SP activity is a negative biomarker for anti-tumor immune response, prognosis, and the response to immunotherapeutic and targeted drugs in pan-cancer.

Graphical abstract, Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients’ clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC50) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein–protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g., asthma and atherosclerosis. Anti-inflammatory drugs come with side effects that may be aggravated by high and fluctuating drug concentrations. To remedy this, an anti-inflammatory drug should have an appropriate pharmacokinetic half-life or better still, a sustained anti-inflammatory drug response. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti-inflammatory response to a common glucocorticoid drug, dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism-a slow release of dexamethasone from the receptor-drug complex. The developed model is in agreement with time-resolved training and testing data and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge-driven drug development to find sustained anti-inflammatory responses and fewer side effects. Funding Agencies|Swedish Research CouncilSwedish Research Council [Dnr 2019-03767]; Heart and Lung FoundationSwedish Heart-Lung Foundation; CENIIT (Center for Industrial Information Technology); Swedish Research CouncilSwedish Research Council; Horizon 2020; KAW and Sci Life Lab Covid-19 platform

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE.

El cáncer de colon es una de las principales causas de muerte a nivel mundial, en este sentido, a pesar de que se ha reducido la tasa de mortalidad, la incidencia en pacientes jóvenes sigue aumentando. El presente trabajo consiste en una revisión bibliográfica descriptiva de la relación existente entre la microbiota, la epigenética y la respuesta a medicamentos utilizados para tratar el cáncer de colon. Dicha revisión es elaborada a partir de la consulta de las bases de datos disponibles en la plataforma del Sistema de Bibliotecas, Documentación e Información (SIBDI) de la Universidad de Costa Rica. El objetivo central de este estudio es entender la relación existente entre la microbiota y la epigenética para así poder usar estos conocimientos en favor de la optimización de la terapia farmacológica y, además, predecir respuestas a medicamentos en el cáncer de colon. Se encontró evidencia que sugiere que el tipo de dieta impacta directamente en la microbiota intestinal, la cual, por su parte, puede tener un efecto pro o anti cancerígeno, dependiendo de las modificaciones epigenéticas dadas por metabolitos de estas bacterias y que pueden modificar la respuesta a la quimioterapia. Se concluye que el cáncer de colon es de origen multifactorial, por lo que se afirma que se ve afectado por eventos genéticos: la dieta, el estilo de vida, la microbiota y sus metabolitos. Finalmente, es promisorio encontrar maneras para asociar concretamente microbiota relacionada con los cambios de respuesta a medicamentos y así optimizar las terapias oncológicas. Globally, colon cancer is one of the main causes of death, although its death rate has decreased, the incidence in young patients is increasing. This research consists in a descriptive bibliographic review of the relationship of the microbiota, epigenetics and the response to drugs used in colon cancer, elaborated from the bibliographic on-line platform (SIBDI) that the University of Costa Rica makes available to researchers. In addition to that, the objective of this study is to understand the relationship between the microbiota and epigenetics to use that knowledge to optimize pharmacological therapy and predict response to colon cancer drugs. Moreover, as a result: evidence was found that supports that the diet impacts directly in the intestinal microbiota and can have pro or anticancer effects depending on the epigenetic modifications given by the metabolites provided by the types of bacteria present in the gut that can modify drug response. Finally, this research concludes that Colon cancer has a multifactorial origin and it can be affected by genetic events, diet, lifestyle, and microbiotas metabolites. It provides promising search for new ways to associate specific bacteria and changes in colon cancer drug response. Universidad de Costa Rica/[]/UCR/Costa Rica UCR::Vicerrectoría de Docencia::Salud::Facultad de Farmacia

Raynaud's phenomenon (RP) is a condition where arterial spasm, usually in the fingers, causes episodes of reduced blood flow. The condition is either idiopathic (primary) or related to a connective tissue disorder or drug response (secondary). We present a case of severe RP where we performed a novel-sequenced CTA and MRA during a prolonged active episode of peripheral vasospasm. Real-time multidisciplinary consultation resulted in appropriate therapy with symptoms alleviation within hours of presentation.

Refractoriness remains as one of the challenges in patients with lymphoma under chemotherapy, and among biological regulators in cells driving this type of response are microRNAs (miRNAs). Different genes are constantly turned on or off according to the miRNAs expression profiles affecting the drug response in patients and their stability in serum and plasma makes them potential prognostic biomarkers in several diseases. Here we described a profile of miRNAs in plasma of diffuse large B cell lymphoma (DLBCL) patients. miRNA expression arrays were carried using pre-treatment plasma samples of sixteen patients, followed by a comparison between the responder and the non-responders. After six cycles of R–CHOP treatment, twelve out of sixteen patients were clinically diagnosed with complete response while in four patients no clinical response was observed. Between these groups, a signature of fifteen differential expressed miRNAs was found. The circulating miRNAs in plasma of patients with no response were related to the drug resistance in other types of cancer, by targeting genes involved in cell proliferation and apoptosis, among other cell processes.

Objectives An efficient pharmacological response to MTX treatment in RA patients relies on the retention and accumulation of intracellular MTX-polyglutamates catalysed by the enzyme folylpolyglutamate synthetase (FPGS). We recently identified a partial retention of FPGS intron 8 (8PR) as a prominent splice variant conferring FPGS dysfunction and decreased MTX polyglutamylation in acute lymphoblastic leukaemia. Here, we explored the association between FPGS 8PR levels and lack of MTX responsiveness in RA patients. Methods Thirty-six patients undergoing MTX treatment were enrolled from the Combinatie behandeling Reumatoide Artritis (COBRA)-light trial. RNA was isolated from blood samples at baseline, 13 weeks and 26 weeks of therapy, from patients in either COBRA-light (n = 21) or COBRA (n = 15) treatment arms. RT-qPCR analysis was used to assess RNA levels of FPGS 8PR over wild-type FPGS (8WT). Results In the COBRA-light treatment arm, higher baseline ratios of 8PR/8WT were significantly associated with higher 44-joint disease activity score (DAS44) at 13 and 26 weeks. Higher baseline ratios of 8PR/8WT also trended towards not obtaining low disease activity (DAS Conclusion This study is the first to associate alterations in FPGS pre-mRNA splicing levels with reduced responsiveness to MTX treatment in RA patients. Trial registration ISRCTN55552928.

Background Prediction of drug response based on multi-omics data is a crucial task in the research of personalized cancer therapy. Results We proposed an iterative sure independent ranking and screening (ISIRS) scheme to select drug response-associated features and applied it to the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we incorporated multi-omics data including copy number alterations, mutation and gene expression and selected up to 50 features using ISIRS. Then a linear regression model based on the selected features was exploited to predict the drug response. Cross validation test shows that our prediction accuracies are higher than existing methods for most drugs. Conclusions Our study indicates that the features selected by the marginal utility measure, which measures the conditional probability of drug responses given the feature, are helpful for drug response prediction.

Recently, several promising treatments have emerged for neuromuscular disorders, highlighting the need for robust biomarkers for monitoring therapeutic efficacy and maintenance of the therapeutic effect. Several studies have proposed circulating and tissue biomarkers, but none of them has been validated to monitor acute and long-term drug response. We previously described how the myostatin (MSTN) level is naturally downregulated in several neuromuscular diseases, including Duchenne muscular dystrophy (DMD). Here, we show that the dystrophin-deficient Golden Retriever muscular dystrophy (GRMD) dog model also presents an intrinsic loss of Mstn production in muscle. The abnormally low levels of Mstn observed in the GRMD dog puppies at 2 months were partially rescued at both mRNA and protein level after adeno-associated virus (AAV)-microdystrophin treatment in a dose-dependent manner. These results show that circulating Mstn is a robust and reliable quantitative biomarker, capable of measuring a therapeutic response to pharmaco-gene therapy in real time in the neuromuscular system, as well as a quantitative means for non-invasive follow-up of a therapeutic effect. Moreover, a 2-year follow-up also suggests that Mstn could be a longitudinal monitoring tool to follow maintenance or decrease of the therapeutic effect., Graphical Abstract, Duchenne muscular dystrophy is a severe muscle disease, and recently, several treatments have emerged highlighting the need for a blood-based biomarker. Here, Dumonceaux and colleagues demonstrated that myostatin, an inhibitor of muscle growth secreted by skeletal muscles, is a reliable and quantitative biomarker for short- and long-term monitoring of therapy effects.

Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.

Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with enhanced pharmacokinetics and pharmacodynamics. Machine learning is a promising in-silico tool used to simulate cells with specific disease properties and to determine their response toward drug uptake. Differences in the properties of normal and infected cells, including biophysical, biochemical and physiological characteristics, plays a key role in developing fundamental cellular probing platforms for machine learning applications. Cellular features can be extracted periodically from both the drug treated, infected, and normal cells via image segmentations in order to probe dynamic differences in cell behavior. Cellular segmentation can be evaluated to reflect the levels of drug effect on a distinct cell or group of cells via probability scoring. This article provides an account for the use of machine learning methods to probe differences in the biophysical, biochemical and physiological characteristics of infected cells in response to pharmacokinetics uptake of drug ingredients for application in cancer, diabetes and neurodegenerative disease therapies.

Objectives To assess the impact of variable drug response and measurement error on SBP control. Methods We simulated a treat-to-target strategy for populations with different pretreatment SBP, whereby medications were added sequentially until measured SBP (mSBP) less than 140 mmHg. Monte Carlo simulations determined variability of both drug response (drugeff ± σdrug; 10 ± 5 mmHg base case) and measurement error (σmeas; 10 mmHg base case) of true SBP (tSBP). The primary outcome measure was the proportion of individuals who achieved target less than 140 mmHg. Results Decision-making based on mSBP resulted in 35.0% of individuals with initial tSBP 150 mmHg being either inappropriately given, or inappropriately denied a second drug. When the simulation was run for multiple drug titrations, measurement error limited tSBP control for all populations tested. A strategy of drug titration based on a second measurement for individuals at risk of incorrect decisions (mSBP 120-150 mmHg; σmeas 15 mmHg) reduced the proportion above target from 40.1 to 30.0% when initial tSBP 160 mmHg. When the measurement variability for the second reading was reduced below that usually seen in clinical practice (σmeas 5 mmHg), the proportion above target decreased further to 17.4%. Conclusion In this simulation, measurement error had the greatest impact on the proportion of individuals achieving their SBP target. Efforts to reduce this error through repeated measures, alternative measurement techniques or changing thresholds, are promising strategies to reduce cardiovascular morbidity and mortality and should be investigated in clinical trials. Here we have shown that Monte Carlo simulations are a useful technique to investigate the influence of uncertainty for different hypertension management strategies.

Bladder cancer is the fourth most common cancer in men, and it is becoming a prevalent malignancy. Most of the regular clinical examinations are prompt evaluations with cystoscopy, renal function testing, which require high-precision instrument, well-trained operators, and high cost. In this study, a microfluidic paper-based analytical device (μPAD) was fabricated to detect nuclear matrix protein 22 (NMP22) and bladder cancer antigen (BTA) from the urine samples. Urine samples were collected from 11 bladder cancer patients and 10 well-beings as experiment and control groups, respectively, to verify the working efficiency of μPAD. A remarkable checkout efficiency of up to 90.91% was found from the results. Meanwhile, this method is feasible for home-based self-detection from urine samples within 10 min for the total process, which provides a new way for quick, economical, and convenient tumor diagnosis, prognosis evaluation, and drug response.

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient’s chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

In this review, we focus on the application of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated nuclease 9 (Cas9), as a powerful genome editing system, in the identification of resistance mechanisms and in overcoming drug resistance in the most frequent solid tumors.Data were collected by conducting systematic searching of scientific English literature using specific keywords such as "cancer", "CRISPR" and related combinations.The review findings revealed the importance of CRISPR/Cas9 system in understanding drug resistance mechanisms and identification of resistance-related genes such as PBRM1, SLFN11 and ATPE1 in different cancers. We also provided an overview of genes, including RSF1, CDK5, and SGOL1, whose disruption can synergize with the currently available drugs such as paclitaxel and sorafenib.The data suggest CRISPR/Cas9 system as a useful tool in elucidating the molecular basis of drug resistance and improving clinical outcomes. Graphical abstract The mechanisms of CRISPR/Cas9-mediated genome editing and double-strand breaks (DSBs) repair.

This study was aimed to investigate the prevalence of CYP2C9*2 (p.430C > T, rs1799853), CYP2C9*3 (p.1075A > C, rs1057910), CYP4F2*3 (p.1297G > A, rs2108622), CYP2C19*2 (p.681G > A, rs4244285), CYP2C19*3 (p.636G > A, rs4986893), CYP2C19*17 (p.1260C > A, rs12248560), ABCB1 (p.3435C > T, rs1045642), CYP2D6*4 (p.1846G > A, rs3892097), SLCO1B1*5 (p.521T > C, rs4149056) and CES1 (p.1168-33A > C, rs2244613) among Tatars and Balkars ethnic groups living in Russia to provide a basis for future clinical studies concerning on understanding of population-level differences in drug response. The study involved 341 apparently healthy, unrelated, and chronic medication-free volunteers of both sexes of ethnic groups of Tatars and Balkars living in Volga and Caucasus regions of Russia. Genotyping was performed using real-time polymerase chain reaction-based methods. The allelic prevalence of studied markers in ethnic groups were compared with Russians as a largest ethnic group in Russia. Statistically significant differences for the following gene polymorphisms were found between both ethnic groups in respect of different markers and with Russians. Our study shows differences in prevalence of the main relevant pharmacogenetic markers in Tatars and Balkars. These findings should be taken into consideration for personalization algorithms development and pharmacogenetics implementation in regions with ethnic minorities as Russia has.

Background:Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood.Objective:The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response.Methods:The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action.Results:HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action.Conclusion:The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action.

Background Treatment resistance is a major clinical challenge of diffuse large B-cell lymphoma (DLBCL) where approximately 40% of the patients have refractory disease or relapse. Since DLBCL is characterized by great clinical and molecular heterogeneity, the purpose of the present study was to investigate whether miRNAs associated to single drug components of R-CHOP can improve robustness of individual markers and serve as a prognostic classifier. Methods Fifteen DLBCL cell lines were tested for sensitivity towards single drug compounds of the standard treatment R-CHOP: rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). For each drug, cell lines were ranked using the area under the dose-response curve and grouped as either sensitive, intermediate or resistant. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs associated to growth response after exposure towards single drugs of R-CHOP. Using the Affymetrix HG-U133 platform, expression levels of miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers predicting disease progression were build using multiple Cox regression or random survival forest. Classifiers were validated and ranked by repeated cross-validation. Results Prognostic accuracies were assessed by Brier Scores and time-varying area under the ROC curves, which revealed better performance of multivariate Cox models compared to random survival forest models. The Cox model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the Cox miRNA-panel and IPI substantially increased prognostic performance in GCB classified patients. Conclusion As a proof of concept, we found that expression data of drug associated miRNAs display prognostic utility and adding these to IPI improves prognostic stratification of GCB-DLBCL patients treated with R-CHOP.

Recent studies on microbiome highlighted their importance in various environments including human, where they are involved in multiple biological contexts such as immune mechanism, drug response, and metabolism. The rapid increase of new findings in microbiome research is partly due to the technological advances in microbiome identification, including the next-generation sequencing technologies. Several applications of different next-generation sequencing platforms exist for microbiome identification, but the most popular method is using short-read sequencing technology to profile targeted regions of 16S rRNA genes of microbiome because of its low-cost and generally reliable performance of identifying overall microbiome compositions. The analysis of targeted 16S rRNA sequencing data requires multiple steps of data processing and systematic analysis, and many software tools have been proposed for such procedures. However, properly organizing and using such software tools still require certain level of expertise with computational environments. The purpose of this article is introducing the concept of computational analysis of 16S rRNA sequencing data to microbiologists and providing easy-to-follow and step-by-step instructions of using recent software tools of microbiome analysis. This instruction may be used as a quick guideline for general next-generation sequencing-based microbiome studies or a template of constructing own software pipelines for customized analysis.

Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug response-related lncRNAs (DRlncs) by combining expression data of lncRNA, microRNA, messenger RNA, methylation levels, somatic mutations, and the survival data of cancer patients treated with drugs. We constructed an integrated and computational multi-omics approach to identify DRlncs for diverse chemotherapeutic drugs in BRCA. Some DRlncs were identified with Adriamycin, Cytoxan, Tamoxifen, and all samples for BRCA patients. These DRlncs showed specific features regarding both expression and computational accuracies. The DRlnc-gene co-expression networks were constructed and analyzed. Key DRlncs, such as HOXA-AS2 (Ensembl: ENSG00000253552), in the drug Adriamycin were characterized. The experimental analysis also suggested that HOXA-AS2 (Ensembl: ENSG00000253552) was a key DRlnc in Adriamycin drug resistance in BRCA patients. Some DRlncs were associated with survival and some specific functions. A possible mechanism of DRlnc HOXA-AS2 (Ensembl: ENSG00000253552) in the Adriamycin drug response for BRCA resistance was inferred. In summary, this study provides a framework for lncRNA-based evaluation of clinical drug responses in BRCA. Understanding the underlying molecular mechanisms of drug responses will facilitate improved responses to chemotherapy and outcomes of BRCA treatment.

Lennox-Gastaut syndrome (LGS) is a severe, childhood-onset, developmental epileptic encephalopathy, with different etiologies and co-morbidities. Seizure treatment in LGS represents a major challenge; new antiepileptic drugs (AEDs) are developed to especially address seizures resulting in high morbidity and mortality, such as drop seizures. Rufinamide (RFN) is one of the latest AEDs licensed for patients with LGS. Its mechanism of action involves sodium channels in a way that is unrelated to other AEDs. Here we discuss the use of adjunctive RFN in children and adolescents with LGS and its efficacy and safety profile, based on a systematic literature review. RFN shows a very favorable profile in terms of adverse events and drug-interactions in children. It is particularly effective on tonic-atonic seizures and spasms, impacting on the quality of life of the patients. Further studies are needed to clarify the interaction profile with the newest AEDs for LGS and to assess correlations between the etiology of LGS and drug response to individualize treatment and maximize efficacy.

Drug response prediction in cancer cell lines is vital to discover new anticancer drugs. However, it's still a challenging task to accurately predict drug responses in cancer cell lines. In this study, we presented a novel computational approach, named as MSDRP (modular within and between score for drug response prediction), to predict drug responses in cell lines. The method is based on a constructed heterogeneous drug-cell line network with multiple information. Compared with other state-of-the-art methods, MSDRP acquired better predictive performance, and identified potential associations between drugs and cell lines, which have been confirmed by the published literature. The source code of MSDRP is freely available at https://github.com/shimingwang1994/MSDRP.git.

Microfluidic lumen-based systems are microscale models that recapitulate the anatomy and physiology of tubular organs. These technologies can mimic human pathophysiology and predict drug response, having profound implications for drug discovery and development. Herein, we review progress in the development of microfluidic lumen-based models from the 2000s to the present. The core of the review discusses models for mimicking blood vessels, the respiratory tract, the gastrointestinal tract, renal tubules, and liver sinusoids, and their application to modeling organ-specific diseases. We also highlight emerging application areas, such as the lymphatic system, and close the review discussing potential future directions.

A unique specimen of argyria is preserved in the Morgagni Museum of Pathological Anatomy at the University of Padua (Italy). It is a stuffed head belonging to a man who decided to cure his syphilis by himself with the so-called infernal stone (silver nitrate) every day for years, thus developing argyria in the second half of the nineteenth century. Paleopathological and historical studies were performed on the specimen to confirm the diagnosis of argyria. Furthermore, a morphological investigation of the specimen was conducted with histological and ultrastructural investigations, including environmental scanning electron microscopy and electron dispersive x‐ray spectroscopy, recording high presence of silver in the dermis and epidermis and also other chemical elements correlated to the “infernal stone.” A comparison with actual cases may also lead to a common feature: a potential dependence on the perceived benefits brought by silver compound that may sustain a further prolonged intake.

Background Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. Methods To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. Results We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. Conclusions Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.

FOLFOX (5‐fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within‐sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F‐score in predicting pathological response of CRC to FOLFOX. The REO‐based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F‐score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F‐score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression‐free survival than the predicted non‐responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5‐fluorouracil‐based signatures. In conclusion, the REO‐based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen., FOLFOX is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. The REO‐based qualitative transcriptional signature can accurately identify CRC patients who may benefit from the FOLFOX regimen.

Tweetable abstract Warfarin will remain in use for a long time to come, thus inclusion of African populations in pharmacogenomics is essential to be able to identify all possible biomarkers that are potential predictors for warfarin drug response.Tweetable abstract Warfarin will remain in use for a long time to come, thus inclusion of African populations in pharmacogenomics is essential to be able to identify all possible biomarkers that are potential predictors for warfarin drug response.

Introduction Baricitinib is an oral synthetic Janus Kinase inhibitor that inhibits JAK1 and JAK2, and the new kid on the block in the treatment of rheumatoid arthritis (RA). To date, there are no studies comparing the clinical benefit of baricitinib in RA between different ethnicities. Ethnicity plays a role in the effectiveness of therapeutic agents. Given the large multi-ethnic population of Leicestershire in the United Kingdom and the range of new therapeutics in RA, we reviewed our cohort of patients with RA to see whether there is any difference in baricitinib Disease Activity Score 28 (DAS28) response between the Asian and White cohorts. Methods This was a retrospective study. The patients included were those under the care of rheumatology at University Hospitals of Leicester (UHL) with a diagnosis of RA and either receiving baricitinib or had received it in the past. Data was collected using the UHL information technology systems, clinic letters and pharmacy records. In addition to ethnicity, we reviewed patient age, gender, concurrent disease-modifying anti-rheumatic drugs (DMARDs) used, previous biologics used, baseline and post-treatment DAS28, dropout from therapy, baseline biochemical assays (anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor (RF) status) and radiographic findings. An independent t-test was used to compare continuous data, and Pearson's chi-squared test was used to compare categorical data. Results A total of 120 patients were included in the analysis, and data were analysed with Portable Format for Analytics (PFA). There was no statistically significant difference in the mean DAS28 at baseline (Asian: 5.17 versus White: 4.65; p-value = 0.107) and post-treatment (Asian: 2.8 versus White: 3.3; p-value = 0.404). Comparing both ethnicities, there was no statistically significant difference in previous biologics used, anti-CCP and RF titres, and radiographic findings of erosions. Conclusion This is the first study of its kind, and it found no significant difference in baricitinib response between the Asian and White cohorts. Our study had certain limitations, and future studies will be needed to evaluate this subject further. Such data is important as it can contribute to a body of evidence that may in the future help inform clinical decision-making.

Background/aim To retrospectively analyze the results of histoculture drug response assays (HDRAs) to determine whether the results could predict platinum sensitivity and prognosis in ovarian cancer. Patients and methods One hundred thirty-nine patients with ovarian cancer were reviewed. HDRAs were conducted for platinum and taxane agents. Platinum resistance and sensitivity occurred in 21 and 118 patients, respectively. To analyze the relationship between the inhibition rates (IRs) of tumor growth caused by the platinum agent and clinical outcomes, Student's t-test and linear regression analysis were used. Results We found that the average IRs of the platinum and taxane agent were not statistically significant between the platinum-sensitive and - resistant groups. There was no statistical significance for overall survival, progression-free survival, or platinum-free interval. Conclusion The HDRA is not useful for predicting platinum sensitivity and survival outcomes.

Motivation Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (i) medical records only contain the response of a patient to very few drugs, (ii) drugs are recommended by doctors based on their expert judgment and (iii) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. Results We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. Availability and implementation The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. Supplementary information Supplementary data are available at Bioinformatics online., Bioinformatics, 34 (16), ISSN:1367-4803, ISSN:1460-2059

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs’ predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

RATIONALE: Blood eosinophil count predicts response to inhaled corticosteroids and specific biologic therapies in selected asthma patients. Despite this important role, fundamental aspects of eosinophil behavior in asthma have not been studied. Objectives To investigate the behavior of blood eosinophils in a population comparing their distribution with the general population and studying their intra-individual variability over time in relation to hospital episodes (emergency department visits and hospitalizations) in clinical practice.METHODS: The distribution and variability of 35,703 eosinophil determinations in 10,059 stable asthma patients were investigated in the Majorca Real-Life Investigation in COPD and Asthma cohort (MAJORICA). Eosinophil distribution in the asthma population was compared with a control sample from the general population of 8,557 individuals. Eosinophil variability and hospital episodes were analyzed using correlations, ROC curves and multiple regression analysis. We defined the Eosinophil Variability Index (EVI) as (Eosmax-Eosmin/Eosmax) x 100%. The findings of the asthma population were re-tested in an external well-characterized asthma cohort.RESULTS: The eosinophil count values and variability were higher in the asthma population than in the general population (p-valueCONCLUSION: The eosinophil variability in asthma patients better identifies the risk of any hospital episode than the absolute counting values currently used to target specific treatments.