Preclinical pharmacokinetic evaluation of novel antimalarial and antituberculosis drug leads

Preclinical pharmacokinetics relies on efficient and accurate screening to select clinical candidates from early leads. Poor pharmacokinetic interpretation can disadvantage drug discovery by promoting inadequate compounds and expelling potential drug candidates. Objectives of this project included pharmacokinetic evaluation of antimalarial and anti-tuberculosis lead compounds with techniques aimed at improving preclinical pharmacokinetic outcomes. This included mechanistic pharmacokinetic approaches such as non-linear mixed effects (NLME) modelling in comparison with traditional non-compartmental analysis. Where appropriate, pharmacokinetic methods were expanded to include organ distribution and capsule dosing in mice to bridge our techniques from discovery to early development. Three benzoxazole amodiaquine analogues possessing equipotent in vitro antiplasmodial activity and showed diverse in vivo efficacy in a malaria mouse model. Evaluation of their respective pharmacokinetics in mice showed their in vivo exposures could translate to in vivo efficacy. Retrospective PK/PD simulations point to a time above IC50 drive in efficacy. Pharmacokinetic evaluation of an aminopyridine antimalarial compound in its cyclodextrin inclusion complex revealed a pH dependent increase in solubility that reduced variance, likely due to favoured intestinal absorption. Investigation of two novel fusidic acid C-3 ester prodrugs aimed at repositioning fusidic acid for tuberculosis, showed high concentrations of the rodent specific 3-epifusidic acid metabolite that greatly reduced exposure of fusidic acid in mice. Further organ distribution studies showed a prodrug strategy is still viable for repositioning fusidic acid for tuberculosis, but that rodent models are inappropriate for further evaluation. NLME modelling successfully provided unique mechanistic and mathematical insight of pharmacokinetic profiles of new leads. The level of interpretation on pharmacology parameters improved and aided in understanding why drug leads are likely to fail or succeed, assisting future compound optimisation

Preclinical pharmacokinetic evaluation of novel antimalarial and antituberculosis drug leads

Preclinical pharmacokinetics relies on efficient and accurate screening to select clinical candidates from early leads. Poor pharmacokinetic interpretation can disadvantage drug discovery by promoting inadequate compounds and expelling potential drug candidates. Objectives of this project included pharmacokinetic evaluation of antimalarial and anti-tuberculosis lead compounds with techniques aimed at improving preclinical pharmacokinetic outcomes. This included mechanistic pharmacokinetic approaches such as non-linear mixed effects (NLME) modelling in comparison with traditional non-compartmental analysis. Where appropriate, pharmacokinetic methods were expanded to include organ distribution and capsule dosing in mice to bridge our techniques from discovery to early development. Three benzoxazole amodiaquine analogues possessing equipotent in vitro antiplasmodial activity and showed diverse in vivo efficacy in a malaria mouse model. Evaluation of their respective pharmacokinetics in mice showed their in vivo exposures could translate to in vivo efficacy. Retrospective PK/PD simulations point to a time above IC50 drive in efficacy. Pharmacokinetic evaluation of an aminopyridine antimalarial compound in its cyclodextrin inclusion complex revealed a pH dependent increase in solubility that reduced variance, likely due to favoured intestinal absorption. Investigation of two novel fusidic acid C-3 ester prodrugs aimed at repositioning fusidic acid for tuberculosis, showed high concentrations of the rodent specific 3-epifusidic acid metabolite that greatly reduced exposure of fusidic acid in mice. Further organ distribution studies showed a prodrug strategy is still viable for repositioning fusidic acid for tuberculosis, but that rodent models are inappropriate for further evaluation. NLME modelling successfully provided unique mechanistic and mathematical insight of pharmacokinetic profiles of new leads. The level of interpretation on pharmacology parameters improved and aided in understanding why drug leads are likely to fail or succeed, assisting future compound optimisation

Design and synthesis of polycyclic amine derivatives for sigma receptor activity

>Magister Scientiae - MScNew therapeutic strategies are needed for a diverse array of poorly understood neurological impairments. These include neurodegenerative disorders such as Parkinson’s disease and Alzheimer’s disease, and the psychiatric disorders such as depression, anxiety and drug dependence. Popular neuropharmacotherapies have focused on dopamine (DA), serotonin (5HT), γ-aminobutric acid (GABA) and glutamate systems (Jupp & Lawrence, 2010). However recent research points to the sigma receptor (σR) as a possible neuromodulatory system. Due to its multi-receptor action, the σR can trigger several significant biological pathways. This indicates its ideal potential as a drug target to effectively minimise drug dosage and potential side effects. Currently there are a limited number of σR ligands available and few possess the selectivity to significantly show σR’s role in neurological processes. Polycyclic amines have shown notable sigma activity and provide an advantageous scaffold for drug design that can improve pharmacodynamic and pharmacokinetic properties (Banister et al., 2010; Geldenhuys et al., 2005). Aryl-heterocycle amine groups were also shown to improve σR activity (Piergentili et al., 2009)

Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies.

Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines

Global dataset on seagrass meadow structure, biomass and production

Seagrass meadows provide valuable socio-ecological ecosystem services, including a key role in climate change mitigation and adaption. Understanding the natural history of seagrass meadows across environmental gradients is crucial to deciphering the role of seagrasses in the global ocean. In this data collation, spatial and temporal patterns in seagrass meadow structure, biomass and production data are presented as a function of biotic and abiotic habitat characteristics. The biological traits compiled include measures of meadow structure (e.g. percent cover and shoot density), biomass (e.g. above-ground biomass) and production (e.g. shoot production). Categorical factors include bioregion, geotype (coastal or estuarine), genera and year of sampling. This dataset contains data extracted from peer-reviewed publications published between 1975 and 2020 based on a Web of Science search and includes 11 data variables across 12 seagrass genera. The dataset excludes data from mesocosm and field experiments, contains 14271 data points extracted from 390 publications and is publicly available on the PANGAEA® data repository (10.1594/PANGAEA.929968; Strydom et al., 2021). The top five most studied genera are Zostera, Thalassia, Cymodocea, Halodule and Halophila (84 % of data), and the least studied genera are Phyllospadix, Amphibolis and Thalassodendron (2.3 % of data). The data hotspot bioregion is the Tropical Indo-Pacific (25 % of data) followed by the Tropical Atlantic (21 %), whereas data for the other four bioregions are evenly spread (ranging between 13 and 15 % of total data within each bioregion). From the data compiled, 57 % related to seagrass biomass and 33 % to seagrass structure, while the least number of data were related to seagrass production (11 % of data). This data collation can inform several research fields beyond seagrass ecology, such as the development of nature-based solutions for climate change mitigation, which include readership interested in blue carbon, engineering, fisheries, global change, conservation and policy

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis.

Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior