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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.

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.

Background 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. Methods 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). Results 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). Conclusion 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.

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.

Background Ivermectin (22,23-dihydroavermectin B 1a : H 2 B 1a ) 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. Methods Ivermectin was incubated in the presence of recombinant human cytochrome P 450 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 max ) observed in pharmacokinetics study samples. These samples were fed to An. stephensi mosquitoes, and their survival and vitality was recorded daily over 3 days. Results Human CYP3A4 metabolised ivermectin more rapidly than CYP3A5. Ivermectin metabolites M1–M8 were predominantly formed by CYP3A4, whereas metabolite M9 (hydroxy-H 2 B 1a ) was mainly produced by CYP3A5. Both desmethyl-H 2 B 1a (M1) and hydroxy-H 2 B 1a (M2) killed all mosquitoes within three days post-feeding, while administration of desmethyl, hydroxy-H 2 B 1a (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 max 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). Conclusion 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”.

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 in vitro , an approach that has largely not lived up to expectations in COVID-19. Cell lines used in in vitro experiments are not necessarily representative of lung tissue. Yet, even if the proposed mode of action is indeed true, viral dynamics in vivo , 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 0 , 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 0 , 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.

Background Residual malaria transmission is the result of adaptive mosquito behavior that allows malaria vectors to thrive and sustain transmission in the presence of good access to bed nets or insecticide residual spraying. These behaviors include crepuscular and outdoor feeding as well as intermittent feeding upon livestock. Ivermectin is a broadly used antiparasitic drug that kills mosquitoes feeding on a treated subject for a dose-dependent period. Mass drug administration with ivermectin has been proposed as a complementary strategy to reduce malaria transmission. Methods A cluster randomized, parallel arm, superiority trial conducted in two settings with distinct eco-epidemiological conditions in East and Southern Africa. There will be three groups: human intervention, consisting of a dose of ivermectin (400 mcg/kg) administered monthly for 3 months to all the eligible population in the cluster (>15 kg, non-pregnant and no medical contraindication); human and livestock intervention, consisting human treatment as above plus treatment of livestock in the area with a single dose of injectable ivermectin (200 mcg/kg) monthly for 3 months; and controls, consisting of a dose of albendazole (400 mg) monthly for 3 months. The main outcome measure will be malaria incidence in a cohort of children under five living in the core of each cluster followed prospectively with monthly RDTs Discussion The second site for the implementation of this protocol has changed from Tanzania to Kenya. This summary presents the Mozambique-specific protocol while the updated master protocol and the adapted Kenya-specific protocol undergo national approval in Kenya. BOHEMIA will be the first large-scale trial evaluating the impact of ivermectin-only mass drug administration to humans or humans and cattle on local malaria transmission Trial registration ClinicalTrials.gov NCT04966702 . Registered on July 19, 2021. Pan African Clinical Trials Registry PACTR202106695877303.

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 (R 2 : 0.99). The kinetics of ivermectin were characterised by an initial lag phase of 18.5 h (CI 90% : 17.0–19.8 h) followed by a slow zero-order elimination rate of 5.5 pg/h (CI 90% : 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 (CI 90% : 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.

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.