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.
Ivermectin is an endectocide that has been used broadly in single dose community campaigns for the control of onchocerciasis and lymphatic filariasis for more than 30 years. There is now interest in ...the potential use of ivermectin regimens to reduce malaria transmission, envisaged as community-wide campaigns tailored to transmission patterns and as complement of the local vector control programme. The development of new ivermectin regimens or other novel endectocides will require integrated development of the drug in the context of traditional entomological tools and endpoints. This document examines the main pharmacokinetic and pharmacodynamic parameters of the medicine and their potential influence on its vector control efficacy and safety at population level. This information could be valuable for trial design and clinical development into regulatory and policy pathways.
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.
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this ...barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.
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.
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.
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.
Ivermectin (22,23-dihydroavermectin B
: H
B
) is an endectocide used to treat worm infections and ectoparasites including lice and scabies mites. Furthermore, survival of malaria transmitting ...Anopheles mosquitoes is strongly decreased after feeding on humans recently treated with ivermectin. Currently, mass drug administration of ivermectin is under investigation as a potential novel malaria vector control tool to reduce Plasmodium transmission by mosquitoes. A "post-ivermectin effect" has also been reported, in which the survival of mosquitoes remains reduced even after ivermectin is no longer detectable in blood meals. In the present study, existing material from human clinical trials was analysed to understand the pharmacokinetics of ivermectin metabolites and feeding experiments were performed in Anopheles stephensi mosquitoes to assess whether ivermectin metabolites contribute to the mosquitocidal action of ivermectin and whether they may be responsible for the post-ivermectin effect.
Ivermectin was incubated in the presence of recombinant human cytochrome P
3A4/5 (CYP 3A4/5) to produce ivermectin metabolites. In total, nine metabolites were purified by semi-preparative high-pressure liquid chromatography. The pharmacokinetics of the metabolites were assessed over three days in twelve healthy volunteers who received a single oral dose of 12 mg ivermectin. Blank whole blood was spiked with the isolated metabolites at levels matching the maximal blood concentration (C
) observed in pharmacokinetics study samples. These samples were fed to An. stephensi mosquitoes, and their survival and vitality was recorded daily over 3 days.
Human CYP3A4 metabolised ivermectin more rapidly than CYP3A5. Ivermectin metabolites M1-M8 were predominantly formed by CYP3A4, whereas metabolite M9 (hydroxy-H
B
) was mainly produced by CYP3A5. Both desmethyl-H
B
(M1) and hydroxy-H
B
(M2) killed all mosquitoes within three days post-feeding, while administration of desmethyl, hydroxy-H
B
(M4) reduced survival to 35% over an observation period of 3 days. Ivermectin metabolites that underwent deglycosylation or hydroxylation at spiroketal moiety were not active against An. stephensi at C
levels. Interestingly, half-lives of M1 (54.2 ± 4.7 h) and M4 (57.5 ± 13.2 h) were considerably longer than that of the parent compound ivermectin (38.9 ± 20.8 h).
In conclusion, the ivermectin metabolites M1 and M2 contribute to the activity of ivermectin against An. stephensi mosquitoes and could be responsible for the "post-ivermectin effect".
Ivermectin inhibits the replication of SARS-CoV-2 in vitro at concentrations not readily achievable with currently approved doses. There is limited evidence to support its clinical use in COVID-19 ...patients. We conducted a Pilot, randomized, double-blind, placebo-controlled trial to evaluate the efficacy of a single dose of ivermectin reduce the transmission of SARS-CoV-2 when administered early after disease onset.
Consecutive patients with non-severe COVID-19 and no risk factors for complicated disease attending the emergency room of the Clínica Universidad de Navarra between July 31, 2020 and September 11, 2020 were enrolled. All enrollments occurred within 72 h of onset of fever or cough. Patients were randomized 1:1 to receive ivermectin, 400 mcg/kg, single dose (n = 12) or placebo (n = 12). The primary outcome measure was the proportion of patients with detectable SARS-CoV-2 RNA by PCR from nasopharyngeal swab at day 7 post-treatment. The primary outcome was supported by determination of the viral load and infectivity of each sample. The differences between ivermectin and placebo were calculated using Fisher's exact test and presented as a relative risk ratio. This study is registered at ClinicalTrials.gov: NCT04390022.
All patients recruited completed the trial (median age, 26 IQR 19–36 in the ivermectin and 21–44 in the controls years; 12 50% women; 100% had symptoms at recruitment, 70% reported headache, 62% reported fever, 50% reported general malaise and 25% reported cough). At day 7, there was no difference in the proportion of PCR positive patients (RR 0·92, 95% CI: 0·77–1·09, p = 1·0). The ivermectin group had non-statistically significant lower viral loads at day 4 (p = 0·24 for gene E; p = 0·18 for gene N) and day 7 (p = 0·16 for gene E; p = 0·18 for gene N) post treatment as well as lower IgG titers at day 21 post treatment (p = 0·24). Patients in the ivermectin group recovered earlier from hyposmia/anosmia (76 vs 158 patient-days; p < 0.001).
Among patients with non-severe COVID-19 and no risk factors for severe disease receiving a single 400 mcg/kg dose of ivermectin within 72 h of fever or cough onset there was no difference in the proportion of PCR positives. There was however a marked reduction of self-reported anosmia/hyposmia, a reduction of cough and a tendency to lower viral loads and lower IgG titers which warrants assessment in larger trials.
ISGlobal, Barcelona Institute for Global Health and Clínica Universidad de Navarra.
Mosquitoes are vectors of major diseases such as dengue fever and malaria. Mass drug administration of endectocides to humans and livestock is a promising complementary approach to current ...insecticide-based vector control measures. The aim of this study was to establish an insect model for pharmacokinetic and drug-drug interaction studies to develop sustainable endectocides for vector control. Female Aedes aegypti mosquitoes were fed with human blood containing either ivermectin alone or ivermectin in combination with ketoconazole, rifampicin, ritonavir, or piperonyl butoxide. Drug concentrations were quantified by LC-MS/MS at selected time points post-feeding. Primary pharmacokinetic parameters and extent of drug-drug interactions were calculated by pharmacometric modelling. Lastly, the drug effect of the treatments was examined. The mosquitoes could be dosed with a high precision (%CV: ≤13.4%) over a range of 0.01-1 μg/ml ivermectin without showing saturation (R2: 0.99). The kinetics of ivermectin were characterised by an initial lag phase of 18.5 h (CI90%: 17.0-19.8 h) followed by a slow zero-order elimination rate of 5.5 pg/h (CI90%: 5.1-5.9 pg/h). By contrast, ketoconazole, ritonavir, and piperonyl butoxide were immediately excreted following first order elimination, whereas rifampicin accumulated over days in the mosquitoes. Ritonavir increased the lag phase of ivermectin by 11.4 h (CI90%: 8.7-14.2 h) resulting in an increased exposure (+29%) and an enhanced mosquitocidal effect. In summary, this study shows that the pharmacokinetics of drugs can be investigated and modulated in an Ae. aegypti animal model. This may help in the development of novel vector-control interventions and further our understanding of toxicology in arthropods.
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