Biology subjects

Penny, M. A.

Publications and source records attributed to Penny, M. A..

3 recordsLinked to original sources

bioRxiv2021

Pre-existing partner-drug resistance facilitates the emergence and spread of artemisinin resistance: a consensus modelling study

BackgroundArtemisinin-resistant genotypes have now emerged a minimum of five times on three continents despite recommendations that all artemisinins be deployed as artemisinin combination therapies (ACTs). Widespread resistance to the non-artemisinin partner drugs in ACTs has the potential to limit the clinical and resistance benefits provided by combination therapy. MethodsUsing a consensus modelling approach with three individual-based mathematical models of Plasmodium falciparum transmission, we evaluate the effects of pre-existing partner-drug resistance and ACT deployment on artemisinin resistance evolution. We evaluate settings where dihydroartemisinin-piperaquine (DHA-PPQ), artesunate-amodiaquine (ASAQ), or artemether-lumefantrine (AL) are deployed as first-line therapy. We use time until 0.25 artemisinin resistance allele frequency (the establishment time) as the primary outcome measure. FindingsHigher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance. Across all scenarios and pre-existing frequencies of partner-drug resistance explored, a 0.10 increase in partner-drug resistance frequency on average corresponded to 0.7 to 5.0 years loss of artemisinin efficacy. However, the majority of reductions in time to artemisinin establishment were observed after the first increment from 0.0 to 0.10 partner-drug resistance genotype frequency. InterpretationPartner-drug resistance in ACTs facilitates the early emergence of artemisinin resistance and is a major public health concern. Higher grade partner-drug resistance has the largest effect, with piperaquine-resistance accelerating early emergence of artemisinin-resistant alleles the most. Continued investment in molecular surveillance of partner-drug resistant genotypes to guide choice of first-line ACT is paramount. FundingBill and Melinda Gates Foundation; Wellcome Trust.

genetics↗

bioRxiv2021

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

BackgroundMalaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the hosts immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. MethodsWe identified mechanistic within-host models of parasite dynamics through a review of published literature. For a subset of these, we reproduced model code and compared descriptive statistics between the models using fitted data. Through simulation and model analysis, we compare and discuss key features of the models, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. ResultsThe assessed within-host malaria models generally replicate infection dynamics in malaria-naive individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. ConclusionsOur study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, we propose that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterisation and large stochasticity which inaccurately represent unknown disease mechanisms.

bioinformatics↗

bioRxiv2021

The competition dynamics of resistant and sensitive infections

Antimicrobial resistance is a major health problem with complex dynamics. Resistance may occur in an area because treated infections mutated and developed resistance, and the proportion of infections in a population may then increase. We developed a novel and flexible model that captures several features of resistance dynamics and competition. The model is able to account for many antimicrobials and thus can generally explore competition dynamics and their impact on pathogens and bacteria. Unlike simpler models, our nested model allows the population of resistant pathogen to smoothly increase or decrease. Time dependent dynamics are incorporated into difference equations which examines the effects of 12 parameters. This enables us to explicitly include three key competition dynamics: the transmission cost of resistance that occurs between hosts, the fitness cost of resistance that occurs within untreated hosts, and the release of this competition (from the fitness cost) that occurs once a host is treated. For malaria, our results suggest that without competitive release, drug resistance does not emerge. However, once emerged, competitive release has little effect, and the best way to mitigate the spread is to ensure that treatment is very effective.

bioinformatics↗