Drug‐induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical manifestations vary, and toxicity may or may not ...appear dose‐dependent. We present several machine‐learning models (decision tree induction, k‐nearest neighbor, support vector machines, artificial neural networks) for the prediction of clinically relevant DILI based solely on drug structure, with data taken from published DILI cases. Our models achieved corrected classification rates of up to 89%. We also studied the association of a drug's interaction with carriers, enzymes and transporters, and the relationship of defined daily doses with hepatotoxicity. The results presented here are useful as a screening tool both in a clinical setting in the assessment of DILI as well as in the early stages of drug development to rule out potentially hepatotoxic candidates.
Drug‐induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. We present several machine learning models (decision tree induction, k‐nearest neighbor, support vector machines, artificial neural networks), with models achieving corrected classification rates of up to 89%. We also present an analysis of the association of a drug's interaction with carriers, enzymes and transporters, and the relationship of defined daily doses with hepatotoxicity.
Drug interactions with other drugs are a well-known phenomenon. Similarly, however, pre-existing drug therapy can alter the course of diseases for which it has not been prescribed. We performed ...network analysis on drugs and their respective targets to investigate whether there are drugs or targets with protective effects in COVID-19, making them candidates for repurposing. These networks of drug-disease interactions (DDSIs) and target-disease interactions (TDSIs) revealed a greater share of patients with diabetes and cardiac co-morbidities in the non-severe cohort treated with dipeptidyl peptidase-4 (DPP4) inhibitors. A possible protective effect of DPP4 inhibitors is also plausible on pathophysiological grounds, and our results support repositioning efforts of DPP4 inhibitors against SARS-CoV-2. At target level, we observed that the target location might have an influence on disease progression. This could potentially be attributed to disruption of functional membrane micro-domains (lipid rafts), which in turn could decrease viral entry and thus disease severity.
As of October 2021, neither established agents (e.g., hydroxychloroquine) nor experimental drugs have lived up to their initial promise as antiviral treatment against SARS-CoV-2 infection. While ...vaccines are being globally deployed, variants of concern (VOCs) are emerging with the potential for vaccine escape. VOCs are characterized by a higher within-host transmissibility, and this may alter their susceptibility to antiviral treatment. Here we describe a model to understand the effect of changes in within-host reproduction number R
, as proxy for transmissibility, of VOCs on the effectiveness of antiviral therapy with molnupiravir through modeling and simulation. Molnupiravir (EIDD-2801 or MK 4482) is an orally bioavailable antiviral drug inhibiting viral replication through lethal mutagenesis, ultimately leading to viral extinction. We simulated 800 mg molnupiravir treatment every 12 h for 5 days, with treatment initiated at different time points before and after infection. Modeled viral mutations range from 1.25 to 2-fold greater transmissibility than wild type, but also include putative co-adapted variants with lower transmissibility (0.75-fold). Antiviral efficacy was correlated with R
, making highly transmissible VOCs more sensitive to antiviral therapy. Total viral load was reduced by up to 70% in highly transmissible variants compared to 30% in wild type if treatment was started in the first 1-3 days post inoculation. Less transmissible variants appear less susceptible. Our findings suggest there may be a role for pre- or post-exposure prophylactic antiviral treatment in areas with presence of highly transmissible SARS-CoV-2 variants. Furthermore, clinical trials with borderline efficacious results should consider identifying VOCs and examine their impact in post-hoc analysis.
Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ...patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes.
In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st ('first wave', n = 198) and September 1st through November 16th 2020 ('second wave', n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC).
Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (- 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85-0.99, PPV = 0.90, NPV = 0.58).
With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.
Abstract Background Optimal antibiotic dosing for Staphylococcus aureus bloodstream infections (BSI) is still controversial. One reason is inter-individual variation in pharmacokinetics, which may be ...influenced by various patient-related factors, particularly in critically ill patients. Objectives To describe the population pharmacokinetics (PopPK) of the antibiotic flucloxacillin in patients with S. aureus BSI. Subsequently, we sought to translate the model into a user-friendly app for generating a priori and a posteriori time–concentration curves and dose recommendations to optimize dosing regimens. Methods Total and unbound flucloxacillin concentrations were included from 49 patients from a prospective cohort study conducted during clinical routine, including non-critically ill and critically ill individuals who received intermittent bolus applications. These data were analysed using non-linear mixed-effects modelling. Results Most patients (98%) were treated with 2 g of flucloxacillin every 4 h. We developed a joint model that simultaneously described total and unbound concentrations. The model included an allometric effect of glomerular filtration rate on clearance and albumin on the albumin dissociation constant. The latter was especially important, as in our population the unbound fraction was higher at 11.5% (16.7% for critically ill patients) compared with reported values of approximately 5%. Based on our joint model, we developed a web-based app for optimizing dosing regimens of flucloxacillin. Conclusions By utilizing data from clinical routine, we were able to create a predictive PopPK model of flucloxacillin and identify influential covariates. The web-based app is currently being validated in a clinical trial.
Several repurposed drugs are currently under investigation in the fight against coronavirus disease 2019 (COVID-19). Candidates are often selected solely by their effective concentrations
, an ...approach that has largely not lived up to expectations in COVID-19. Cell lines used in
experiments are not necessarily representative of lung tissue. Yet, even if the proposed mode of action is indeed true, viral dynamics
, host response, and concentration-time profiles must also be considered. Here we address the latter issue and describe a model of human SARS-CoV-2 viral kinetics with acquired immune response to investigate the dynamic impact of timing and dosing regimens of hydroxychloroquine, lopinavir/ritonavir, ivermectin, artemisinin, and nitazoxanide. We observed greatest benefits when treatments were given immediately at the time of diagnosis. Even interventions with minor antiviral effect may reduce host exposure if timed correctly. Ivermectin seems to be at least partially effective: given on positivity, peak viral load dropped by 0.3-0.6 log units and exposure by 8.8-22.3%. The other drugs had little to no appreciable effect. Given how well previous clinical trial results for hydroxychloroquine and lopinavir/ritonavir are explained by the models presented here, similar strategies should be considered in future drug candidate prioritization efforts.
(CR) extracts contain diverse constituents such as saponins. These saponins, which act as a defense against herbivores and pathogens also show promise in treating human conditions such as heart ...failure, pain, hypercholesterolemia, cancer, and inflammation. Some of these effects are mediated by activating AMP-dependent protein kinase (AMPK). Therefore, comprehensive screening for activating constituents in a CR extract is highly desirable. Employing machine learning (ML) techniques such as Deep Neural Networks (DNN), Logistic Regression Classification (LRC), and Random Forest Classification (RFC) with molecular fingerprint MACCS descriptors, 95 CR constituents were classified. Calibration involved 50 randomly chosen positive and negative controls. LRC achieved the highest overall test accuracy (90.2%), but DNN and RFC surpassed it in precision, sensitivity, specificity, and ROC AUC. All CR constituents were predicted as activators, except for three non-triterpene compounds. The validity of these classifications was supported by good calibration, with misclassifications ranging from 3% to 17% across the various models. High sensitivity (84.5-87.2%) and specificity (84.1-91.4%) suggest suitability for screening. The results demonstrate the potential of triterpene saponins and aglycones in activating AMP-dependent protein kinase (AMPK), providing the rationale for further clinical exploration of CR extracts in metabolic pathway-related conditions.
Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new
Salmonella
mutagenicity dataset with 8,290 unique chemical ...structures utilizing MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the
Salmonella
mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest.
•Most protein kinase inhibitors have a high hepatotoxic probability.•High hepatotoxic probability correlates with findings in the literature.•Target specific differences in hepatotoxic potential are ...found.•Protein kinase inhibitors are structurally very similar to each other.
Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Protein kinase inhibitors (PKIs) are generally well tolerated, but unexpected and serious adverse events on the heart, lung, kidney and liver were observed clinically. In this study, the structure-activity relationship of PKIs in relation to hepatotoxicity was investigated. A dataset of 165 PKIs was compiled and the probability of human hepatotoxicity with two different machine learning algorithms (Random Forest and Artificial Neural Networks) was analysed. The estimated probability of hepatotoxicity was generally high for single PKIs. However, depending on the target kinase of the PKI, a difference in hepatotoxic potential could be observed. The similarity of the PKIs to each other is caused by the conserved site of action of the protein kinases. Hepatotoxicity may therefore always be an issue in PKIs.
NSAIDs and paracetamol are commonly used as antipyretic treatments, which may impair renal and hepatic function, respectively. Both organ systems are also negatively affected by COVID-19. In two ...retrospective case–control studies, we investigated whether COVID-19 is a risk factor for the development of renal or hepatic function impairment after NSAID and paracetamol use, respectively. In the NSAID study, we defined cases as patients with a decrease of ≥15% in the estimated glomerular filtration rate (eGFR). We matched them using a 1:2 ratio with controls who did not show a decrease in the eGFR. For the paracetamol study, we matched patients with ALT or ALP ≥ 3x, the upper limits of normal, using a 1:3 ratio with controls whose liver enzymes did not increase. In both studies, we selected demographic data, comorbidities, drug doses, and laboratory values as predictors in addition to SARS-CoV-2 test status. We applied different machine learning models to predict renal and hepatic function impairment. From the cohort of 12,263 unique adult inpatients, we found 288 cases of renal function impairment, which were matched with 576 controls, and 213 cases of liver function impairment, which were matched with 639 controls. In both case–control studies, testing positive for SARS-CoV-2 was not an independent risk factor for the studied adverse drug effects.
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