Biology subjects

Hastings, I.

Publications and source records attributed to Hastings, I..

11 recordsLinked to original sources

bioRxiv2026

Antigenic characterization of SARS-CoV-2 variants BA.3.2.1 and BA.3.2.2 in three animal models

BA.3.2, a variant of SARS-CoV-2 containing [~]40 mutations in its spike protein compared to its nearest ancestor, has spread globally since its first detection in South Africa in November 2024. Here, we report antigenic characterization of BA.3.2 viruses in three naive animal models, and visualize its antigenic phenotype in the context of SARS-CoV-2 evolution using antigenic cartography. We find that: (1) BA.3.2 is substantially antigenically divergent from existing SARS-CoV-2 variants; (2) infection with BA.3.2 in hamster and mouse animal models produces sera with lower homologous titer than infection with other variants. Both of these results may have implications for the selection of vaccine antigens.

microbiology↗

bioRxiv2024

Household level insecticide deployments (micro-mosaics) for insecticide resistance management: Evaluating deliberate and accidental deployments

Mixtures of two insecticides in a single formulation at full-dose are frequently evaluated as the "best" insecticide resistance (IRM) strategy in public health. However, this requires both insecticides to be mixed together in a single formulation which may not be possible or practical. Deploying different insecticides in different households ("micro-mosaics") may allow for mosquitoes to encounter different insecticides in subsequent gonotrophic cycles obtaining a "temporal mixture". We evaluate micro-mosaics considering their deliberate use and their accidental use using a mathematical model assuming polygenic resistance. Deliberate micro-mosaics are evaluated against rotations and mixtures (full-dose or half-dose) over a range of scenarios allowing for cross resistance and insecticide decay on their ability to slow the development of resistance. Accidental micro-mosaics are evaluated to understand the implication of mixture insecticide-treated nets (ITNs) and standard (pyrethroid only) ITNs being deployed alongside one another on the development of resistance across a range of initial resistance scenarios. Deliberate micro-mosaics are found to not differ substantially in their IRM capability from either rotations or half-dose mixtures. When micro-mosaics do outperform rotations or half-dose mixtures the benefit is often small. Micro-mosaics are found to perform worse than full-dose mixtures. Accidental micro-mosaics are found to reduce the ability of mixtures to slow the development of resistance. The deployment of deliberate micro-mosaics was found to not be beneficial versus rotations or mixtures indicating this strategy should not be pursued. Micro-mosaics occurring accidentally due to multiple distribution channels inhibits the effectiveness of mixture ITNs in slowing the development of resistance. Where mixture ITNs are used keep the coverage of the mixture high relative to standard (pyrethroid-only) ITNs is key.

evolutionary biology↗

bioRxiv2024

Combinations of Insecticide-Treated Nets and Indoor Residual Spraying for Insecticide Resistance Management: A Modelling Exploration

Insecticides are heavily used for the control of vectors of disease. Malaria control has been reliant on insecticide treated nets (ITNs) and indoor residual spraying (IRS). There are concerns insecticide resistance will impede malaria control. The use of insecticide resistance management (IRM) strategies is recommended. One proposed IRM strategy is the combination of ITNs and IRS. Using a mathematical model of polygenic insecticide resistance evolution, this combination strategy is evaluated. First, combinations are evaluated against ITNs alone to determine if and when combinations may be beneficial in slowing resistance evolution to the pyrethroid on the ITN. Second combinations where multiple IRS insecticides are available are compared against full-dose mixture ITNs. Results of the simulations indicate the addition of IRS to ITNs may be beneficial, providing coverage of both interventions is high. The greater number IRS insecticides available for rotation the better, however even when combinations rotate three different IRS insecticides this is still a less potent IRM strategy than deploying full-dose mixtures. In conclusion the combination of ITNs and IRS appears to offer limited benefit over full-dose mixture ITNs for an IRM perspective

evolutionary biology↗

bioRxiv2024

Exploring operational requirements of mixtures for insecticide resistance management in public health using a mathematical model assuming polygenic resistance.

Long-lasting insecticide treated nets (LLINs) have been developed which contain two active ingredients. Mixture products for vector control are now an available insecticide resistance management (IRM) strategy. There is a theoretical concern around the use mixtures pertaining to dosing, insecticide decay, initial resistance, and cross resistance. Mixture LLINs all currently have a pyrethroid as one of the partner insecticides. Using previously described mathematical models of polygenic insecticide resistance evolution, which implement selection either by truncation ("polytrucate) or as a probabilistic process ("polysmooth") mixtures are evaluated for their IRM potential. Scenarios are developed to explore the impact of the initial levels of resistance to the pyrethroid insecticide, insecticide decay rates, and insecticide doses, and cross resistance. Results from our simulations indicate that mixtures should be deployed at full doses. As the initial level of resistance to the pyrethroid increases the benefit of mixture decreases. Insecticide decay was found to be less important than might be thought, with other variables having a greater impact. The mechanism of selection demonstrated consistent results, diverging only at very high levels of resistance to the pyrethroid. Our simulations demonstrate that the impact of positive cross resistance is best mitigated using full-dose mixtures. Insecticide decay less important than previously considered, and opens up the opportunity to mix a wider variety of insecticides. However, as mixtures remain a challenge to develop and therefore strategies which could generate an effect of a "temporal" mixture should be evaluated.

evolutionary biology↗

bioRxiv2024

The impact of insecticide decay on the rate of insecticide resistance evolution for monotherapies and mixtures.

The issue of insecticide decay in the public health deployments of insecticides is frequently highlighted as an issue for disease control. There are additional concerns insecticide decay also impacts the selection for insecticide resistance. Despite these concerns insecticide decay is lacking from models evaluating insecticide resistance management strategies. The impact of insecticide decay is modelled using a model which assumes a polygenic basis of insecticide resistance. Single generation selection events covering the insecticide efficacy and insecticide resistance space for both monotherapies and mixtures are conducted. With the outcome being the between generation change in the bioassay survival to the insecticides. The monotherapy sequence strategy and mixture strategy were compared against each other when including insecticide decay, with the outcome being the difference in strategy lifespan. The results demonstrate that as insecticides decay, they can apply a greater selection pressure than newly deployed insecticides, a process which can occur for both monotherapies and mixtures. For mixtures, it is seen that the rate of selection is highest when both insecticides are at reduced efficacies which would occur if reduced dose mixtures were used. Inclusion of insecticide decay in simulations was found to reduce the benefit of mixtures against monotherapy sequences, and this is especially so when reduced-dose mixtures are used. Insecticide decay is often highlighted as an important consideration for mixtures. The inclusion of insecticide decay in models is often lacking, and these results indicate this is absence may be over-inflating the performance of full-dose mixtures. As insecticides decay, they still provide selection pressures with reduced ability to control transmission, replenishing worn-out insecticides more frequently should be considered.

evolutionary biology↗

bioRxiv2023

How best to co-deploy insecticides to minimise selection for resistance.

Insecticides are widely used to control the insects that spread human infectious diseases, in particular falciparum malaria. This widespread use has driven insecticide resistance (IR) to high levels that may threaten the effectiveness of future control programmes. There is interest in identifying deployment methods that alleviate the pressures driving IR and we investigate three. Mixtures are, as already known, highly effective in slowing IR providing their effectiveness (ability to kill fully sensitive insects) remain close to 100%. Mixtures may be expensive and/or operationally difficult so two alternatives to mixtures were investigated. Panels, where different insecticides are physically closely adjacent, for examples, different panels on the same bednet; mosquitoes may therefore encounter both insecticides in the same foraging cycle. Micro-mosaics where different insecticides are deployed in slightly wider geographic proximity, for example in adjacent dwellings. The mosquitoes are unlikely to encountered both insecticides in the same foraging cycle but may encounter different insecticides in subsequent foraging. It is hoped that panels and/or micro-mosaics may, by allowing individual mosquitoes to potentially encounter both insecticides, be effective, lower-cost alternatives to mixtures. Our results suggest this is unlikely to be the case. When insecticides are fully effective then mixtures remain clearly the best strategy. As effectiveness falls then all three strategies are roughly equal. The operational decision of what deployment methods to use depends on how confident we are that insecticides will have high effectiveness that will be maintained in realistic field conditions post-deployment.

genetics↗

bioRxiv2022

Insecticide resistance management strategies for public health control of mosquitoes exhibiting polygenic resistance: a comparison of sequences, rotations, and mixtures.

Malaria control uses insecticides to kill Anopheles mosquitoes. Recent successes in malaria control are threatened by increasing levels of insecticide resistance (IR), requiring insecticide resistance management (IRM) strategies to mitigate this problem. Field trials of IRM strategies are usually prohibitively expensive with long timeframes, and mathematical modelling is often used to evaluate alternative options. Previous IRM models in the context of malaria control assumed IR to have a simple (monogenic) basis, whereas in natural populations, IR will often be a complex polygenic trait determined by multiple genetic variants. A quantitative genetics model was developed to model IR as a polygenic trait. The model allows insecticides to be deployed as sequences (continuous deployment until a defined withdrawal threshold, termed "insecticide lifespan", as indicated by resistance diagnosis in bioassays), rotations (periodic switching of insecticides), or full-dose mixtures (two insecticides in one formulation). These IRM strategies were compared based on their "strategy lifespan" (capped at 500 generations). Partial rank correlation and generalised linear modelling was used to identify and quantify parameters driving the evolution of resistance. Random forest models were used to identify parameters offering predictive value for decision-making. Deploying single insecticides as sequences or rotations usually made little overall difference to their "strategy lifespan", though rotations displayed lower mean and peak resistances. Deploying two insecticides in a full-dose mixture formulation was found to extend the "strategy lifespan" when compared to deploying each in sequence or rotation. This pattern was observed regardless of the level of cross resistance between the insecticides or the starting level of resistance. Statistical analysis highlighted the importance of insecticide coverage, cross resistance, heritability, and fitness costs for selecting an appropriate IRM strategy. Full-dose mixtures appear the most promising of the strategies evaluated, with the longest "strategy lifespans". These conclusions broadly corroborate previous results from monogenic models. Author SummaryInsecticides impregnated into bed-nets or sprayed on walls are used to kill the Anopheles mosquitoes which transmit malaria. Unfortunately, the usage of insecticides has inevitably led to mosquitoes evolving resistance to the toxic effect of these insecticides. Insecticide resistance management strategies may be used to slow the rate of resistance evolution, however which strategies are effective, and when they are effective, is often unclear. Previous models evaluating insecticide resistance management strategies have assumed resistance is encoded by a single gene (is a monogenic trait). However, in natural populations resistance may be determined by multiple genes (is a polygenic trait). It is unclear whether such increased model complexity may change predictions We modelled resistance as a polygenic trait and found little difference in the benefit between rotating insecticides regularly versus deploying continuously until resistance reaches a critical threshold then switching. In contrast, mixtures combining two insecticides extended the projected lifespan of the insecticides, even when they share resistance mechanisms (cross resistance). Similar findings from previous monogenic models, strengthen support for the use of full-dose mixtures.

evolutionary biology↗

bioRxiv2022

Evaluating rotations as a strategy to delay the evolution of insecticide resistance in vectors of human diseases.

IntroductionInsecticide resistance threatens the control of important human vector-borne diseases such as malaria and dengue. The current de facto strategy is to target the insect vectors using a sequential deployment of insecticides i.e. use one insecticide (usually the cheapest available) until resistance has made it ineffective and then replace it with the next insecticide in the repertoire. Rotations of insecticides are often advocated as a potentially superior method of using the insecticide repertoire to delay the evolution of resistance. Testing this hypothesis in the field is logistically demanding and an in silico approach offers a much faster, flexible and transparent method of evaluating rotations. MethodsWe develop an in silico approach to evaluate rotations using sequential deployment as the baseline. We explored a wide range of deployment scenarios, underlying genetics of resistance, and incorporated costs of resistance and gene flow to/from untreated refugia. ResultsWe found that, under most circumstances, resistance to all the insecticides in the repertoire were reached at very similar times for rotations and sequences. Any advantages of one strategy over the other tended to be small (typically <10%) and unpredictable. ConclusionsOperational factors, such as cost or supply-chain security, may therefore largely determine the optimal choice of insecticide deployment strategies.

evolutionary biology↗

bioRxiv2021

Should deep-sequenced amplicons become the new gold-standard for analysing malaria drug clinical trials?

BackgroundRegulatory clinical trials are required to ensure the continued supply and deployment of effective antimalarial drugs. Patient follow-up in such trials typically lasts several weeks as the drugs have long half-lives and new infections often occur during this period. "Molecular correction" is therefore used to distinguish drug failures from new infections. The current WHO-recommend method for molecular correction uses length-polymorphic alleles at highly diverse loci but is inherently poor at detecting low density clones in polyclonal infections. This likely leads to substantial underestimates of failure rates, delaying the replacement of failing drugs with potentially lethal consequences. Deep sequenced amplicons (AmpSeq) substantially increase the detectability of low-density clones and may offer a new "gold standard" for molecular correction. MethodsPharmacological simulation of clinical trials was used to evaluate the suitability of AmpSeq for molecular correction. We investigated the impact of factors such as the number of amplicon loci analysed, the informatics criteria used to distinguish genotyping noise from real low density signals, the local epidemiology of malaria transmission, and the potential impact of genetic signals from gametocytes. ResultsAmpSeq greatly improved molecular correction and provided accurate drug failure rate estimates. The use of 3 to 5 amplicons was sufficient, and simple, non-statistical, criteria could be used to classify recurrent infections as drug failures or new infections. ConclusionsThese results strongly endorse the deployment of AmpSeq as the standard for molecular correction in regulatory trials, with its potential extension into routine surveillance once the requisite technical support becomes established.

microbiology↗

bioRxiv2019

Incorporating genetic selection into individual-based models (IBMs) of malaria and other infectious diseases.

Optimal control strategies for human infections are often investigated by computational approaches using individual-based models (IBMs). These typically track humans and evaluate the impact of control interventions in terms of human deaths, clinical cases averted, interruption of transmission etc. Genetic selection can be incorporated into these IBMs and used to track the spread of mutations whose origin and spread are often driven by the intervention, and which subsequently undermine the control strategy; for example, mutations which encode antimicrobial drug resistance or diagnosis- or vaccine-escape phenotypes. Basic population genetic descriptions of selection are based on infinite population sizes (so that chance fluctuations in allele frequency are absent) but IBMs track finite population sizes. We describe how the finite sizes of IBMs affect simulating the dynamics of genetic selection and how best to incorporate genetic selection into these models. We use the OpenMalaria IBM of malaria as an example, but the same principles apply to IBMs of other diseases. We identify four strategies to incorporate selection into IBMs and make the following four recommendations. Firstly, calculate and report the selection coefficients, s, of the advantageous allele as the key genetic parameter. Secondly, use these values of s to calculate the wait-time until a mutation successful establishes itself in the population. The wait time for the mutation can be added to speed of selection, s, to calculate when the mutation will reach significant, operationally important levels. Thirdly, quantify the ability of the IBM to robustly estimate small selection coefficients. Fourthly, optimise computational efficacy: when s is small it is plausible that fewer replicates of larger IBMs will be more efficient than a larger number of replicates of smaller size.

evolutionary biology↗

bioRxiv2019

Evaluating artesunate-amodiaquine deployment, efficacy and safety: an in silico pharmacological model

BackgroundThe World Health Organization currently recommends artesunate-amodiaquine (AS-AQ) as a first-line treatment for uncomplicated falciparum malaria. The clinical efficacy of AS-AQ is very high but its effectiveness in the field varies considerably. This study aimed at comparing the efficacy, effectiveness and safety of AS-AQ fixed dose combination (FDC) and non-fixed formulation (non-FDC) in controlled and real-life settings using a pharmacological model of antimalarial treatment.\n\nMethodsThe effectiveness and safety of different drug formulations in different treatment scenarios were investigated using a pharmacological model of AS-AQ treatment. The model simulated multiple treatment scenarios to assess the effects of age-or weight-based dosing bands in three geographically distinct patient populations, and poor patient adherence.\n\nResultsThe model output was consistent with clinical trials in terms of cure rates, recrudescence rates and the pattern of AQ overdosing with age- and weight-based dosing regimens. AS-AQ treatment has good efficacy and effectiveness in fully adherent patients but monotherapy of AS or AQ lead to treatment failure. The weight-based dosing regimen with FDC was the best option for patients in terms of drug safety and had similar efficacies to the other regimens. Asians were more likely to be overdosed with AQ when using age-based dosing regimens.\n\nConclusionsWeight-based dosing is optimal but not always feasible, so age-based dosing regimens are often used as an alternative. The model outputs highlight the importance of optimising these age-based dosing regimens for specific regions, and identify an increased risk of overdosing in young children.

pharmacology and toxicology↗