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Aims The impact of ageing on antiretroviral pharmacokinetics remains uncertain, leading to missing dosing recommendations for elderly people living with human immunodeficiency virus (HIV: PLWH). The objective of this study was to investigate whether ageing leads to clinically relevant pharmacokinetic changes of antiretrovirals that would support a dose adjustment based on the age of the treated PLWH. Methods Plasma concentrations for 10 first-line antiretrovirals were obtained in PLWH ≥55 years, participating in the Swiss HIV Cohort Study, and used to proof the predictive performance of our physiologically based pharmacokinetic (PBPK) model. The verified PBPK model predicted the continuous effect of ageing on HIV drug pharmacokinetics across adulthood (20-99 years). The impact of ethnicity on age-related pharmacokinetic changes between whites and other races was statistically analysed. Results Clinically observed concentration-time profiles of all investigated antiretrovirals were generally within the 95% confidence interval of the PBPK simulations, demonstrating the predictive power of the modelling approach used. The predicted decline in drug clearance drove age-related pharmacokinetic changes of antiretrovirals, resulting in a maximal 70% [95% confidence interval: 40%, 120%] increase in antiretrovirals exposure across adulthood. Peak concentration, time to peak concentration and apparent volume of distribution were predicted to be unaltered by ageing. There was no statistically significant difference of age-related pharmacokinetic changes between studied ethnicities. Conclusion Dose adjustment for antiretrovirals based on the age of male and female PLWH is a priori not necessary in the absence of severe comorbidities considering the large safety margin of the current first-line HIV treatments.

BackgroundVaccinations have reduced severe burden of COVID-19 and allowed for lifting of non-pharmaceutical interventions. However, with immunity waning alongside emergence of more transmissible variants of concern, vaccination strategies must be examined.MethodsHere we apply a SARS-CoV-2 transmission model to identify preferred frequency, timing, and target groups for vaccine boosters to minimise public health burden and health systems risk. We estimated new infections and hospital admissions averted over two-years through annual or biannual boosting of those eligible (those who received doses one and two) who are 1) most vulnerable (60+ or persons with comorbidities) or 2) those 5+, at universal (98% of eligible) or lower coverage (85% of those 50+ or with comorbidities and 50% of 5−49-year-olds who are eligible) representing moderate vaccine fatigue and/or hesitancy. We simulated three emerging variant scenarios: 1) no new variants; 2) 25% more infectious and immune-evading, Omicron-level severity, variants emerge annually and become dominant; and 3) emerge biannually. We further explored the impact of varying seasonality, variant severity, timing, immune evasion, and infectivity, and vaccine infection blocking assumptions.ResultsTo minimise COVID-19-related hospitalisations over the next two years, boosters should be provided for all those eligible annually three-four months ahead of peak winter whether or not new variants of concern emerge. Only boosting those most vulnerable is unlikely to ensure reduced stress on health systems. Moreover, boosting all eligible protects those most vulnerable more than only boosting the vulnerable group. Conversely, more hospitalisations could be averted per booster dose through annual boosting of those most vulnerable versus all eligible, an indication of cost-effectiveness. Whereas increasing to biannual boosting showed diminishing returns. Results were robust when key model parameters were varied. However, we found that the more frequently variants emerge, the less the effect boosters will have, regardless of whether administered annually or biannually.ConclusionsWell-timed and targeted vaccine boosters preferencing vulnerable, and if possible, all those eligible to receive boosters, can minimise infections and hospital admissions. Findings provide model-based evidence for decision-makers to plan for administering COVID-19 boosters ahead of winter 2022−2023 to help mitigate the health burden and health system stress.

The combination antimalarial therapy of artemisinin-naphthoquine (ART-NQ) was developed as a single-dose therapy, aiming to improve adherence relative to the multiday schedules of other artemisinin combination therapies. The pharmacokinetics of ART-NQ has not been well characterized, especially in children. A pharmacokinetic study was conducted in adults and children over 5 years of age (6 to 10, 11 to 17, and >/=18 years of age) with uncomplicated malaria in Tanzania. The median weights for the three age groups were 20, 37.5, and 55 kg, respectively. Twenty-nine patients received single doses of 20 mg/kg of body weight for artemisinin and 8 mg/kg for naphthoquine, and plasma drug concentrations were assessed at 13 time points over 42 days from treatment. We used nonlinear mixed-effects modeling to interpret the data, and allometric scaling was employed to adjust for the effect of body size. The pharmacokinetics of artemisinin was best described by one-compartment model and that of naphthoquine by a two-compartment disposition model. Clearance values for a typical patient (55-kg body weight and 44.3-kg fat-free mass) were estimated as 66.7 L/h (95% confidence interval [CI], 57.3 to 78.5 L/h) for artemisinin and 44.2 L/h (95% CI, 37.9 to 50.6 L/h) for naphthoquine. Nevertheless, we show via simulation that patients weighing >/=70 kg achieve on average a 30% lower day 7 concentration compared to a 48-kg reference patient at the doses tested, suggesting dose increases may be warranted to ensure adequate exposure. (This study has been registered at ClinicalTrials.gov under identifier NCT01930331.).

The effectiveness of artemisinin-based combination therapies (ACTs) to treat

BACKGROUND: Artemisinin-resistant genotypes of Plasmodium falciparum have now emerged a minimum of six 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. We aimed to model and evaluate the long-term effects of high levels of partner-drug resistance on the early emergence of artemisinin-resistant genotypes. METHODS: Using a consensus modelling approach, we used three individual-based mathematical models of Plasmodium falciparum transmission to evaluate the effects of pre-existing partner-drug resistance and ACT deployment on the evolution of artemisinin resistance. Each model simulates 100 000 individuals in a particular transmission setting (malaria prevalence of 1%, 5%, 10%, or 20%) with a daily time step that updates individuals' infection status, treatment status, immunity, genotype-specific parasite densities, and clinical state. We modelled varying access to antimalarial drugs if febrile (coverage of 20%, 40%, or 60%) with one primary ACT used as first-line therapy: dihydroartemisinin-piperaquine (DHA-PPQ), artesunate-amodiaquine (ASAQ), or artemether-lumefantrine (AL). The primary outcome was time until 0·25 580Y allele frequency for artemisinin resistance (the establishment time). FINDINGS: Higher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance. Across all models, a 10-fold increase in the frequency of partner-drug resistance genotypes on average corresponded to loss of artemisinin efficacy 2-12 years earlier. Most reductions in time to artemisinin resistance establishment were observed after an increase in frequency of the partner-drug resistance genotype from 0·0 to 0·10. INTERPRETATION: Partner-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 the 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. FUNDING: Schmidt Science Fellowship in partnership with the Rhodes Trust; Bill & Melinda Gates Foundation; Wellcome Trust.

L9LS, a potent and safe antimalarial monoclonal antibody, demonstrated 88% protective efficacy against infection in a phase 1 trial in healthy adults.(1) These promising results are the first of many to usher in a potential new era of malaria prevention.

SummarySARS-CoV-2 variant Omicron (B.1.1.529) was classified as a variant of concern (VOC) on November 26, 2021. (1, 2) The infectivity, severity, and immune evasion properties of Omicron relative to the Delta variant would determine 1) the probability of dominant future transmission, and 2) the impact on disease burden. (3, 4) Here we apply individual-based transmission model OpenCOVID to identify thresholds for Omicron’s or any VOC’s potential future dominance, impact on health, and risk to health systems; and identify for which combinations of viral properties, current interventions would be sufficient to control transmission. We show that, with first-generation SARS-CoV-2 vaccines (5) and limited physical distancing in place, the threshold for Omicron’s future dominance was primarily be driven by its degree of infectivity. However, we identified that a VOC’s potential dominance will not necessarily lead to increased public health burden. Expanded vaccination, that includes a third-dose for adults and child vaccination strategies, was projected to have the biggest public health benefit for a highly infective, highly severe VOC with low immune evasion capacity. However, a highly immune evading variant that becomes dominant would likely require alternative measures for control, such as strengthened physical distancing measures, novel treatments, and second-generation vaccines. These findings provide quantitative guidance to decision-makers at a critical time while Omicron’s properties are being assessed and preparedness for new VOC’s is eminent. (6) We emphasize the importance of both genomic and population epidemiological surveillance.

Emerging drug resistance and high-attrition rates in early and late stage drug development necessitate accelerated development of antimalarial compounds. However, systematic and meaningful translation of drug efficacy and host-parasite dynamics between preclinical testing stages is missing. We developed an ensemble of mathematical within-host parasite growth and antimalarial action models, fitted to extensive data from four antimalarials with different modes of action, to assess host-parasite interactions in two preclinical drug testing systems of murine parasite P. berghei in mice, and human parasite P. falciparum in immune-deficient mice. We find properties of the host-parasite system, namely resource availability, parasite maturation and virulence, drive P. berghei dynamics and drug efficacy, whereas experimental constraints primarily influence P. falciparum infection and drug efficacy. Furthermore, uninvestigated parasite behavior such as dormancy influences parasite recrudescence following non-curative treatment and requires further investigation. Taken together, host-parasite interactions should be considered for meaningful translation of pharmacodynamic properties between murine systems and for predicting human efficacious treatment.

1.AbstractSeasonal malaria chemoprevention (SMC) has proven highly efficacious in reducing malaria incidence. However, the continued success of SMC is threatened by the spread of resistance against one of its main preventive ingredients, Sulfadoxine-Pyrimethamine(SP), operational challenges in delivery, and incomplete adherence to the regimens. Via a simulation study with an individual-based model of malaria dynamics, we provide quantitative evidence to assess long-acting injectables (LAIs) as potential alternatives to SMC. We explored the predicted impact of a range of novel preventive LAIs as a seasonal prevention tool in children aged three months to five years old during late-stage clinical trials and at implementation. LAIs were co-administered with a blood-stage clearing drug once at the beginning of the transmission season. We found the establishment of non-inferiority of LAIs to standard 3 or 4 rounds of SMC with SP-amodiaquine was challenging in clinical trial stages due to high intervention deployment coverage. However, our analysis of implementation settings where the achievable SMC coverage was much lower, LAIs with fewer visits per season are potential suitable replacements to SMC. Suitability as a replacement with higher impact is possible if the duration of protection of LAIs covered the duration of the transmission season. Furthermore, optimizing LAIs coverage and protective efficacy half-life via simulation analysis in settings with an SMC coverage of 60% revealed important trade-offs between protective efficacy decay and deployment coverage. Our analysis additionally highlights that for seasonal deployment for LAIs, it will be necessary to investigate the protective efficacy decay as early as possible during clinical development to ensure a well-informed candidate selection process.

As vaccination coverage against SARS-CoV-2 increases amidst the emergence and spread of more infectious and potentially more deadly viral variants, decisions on timing and extent of relaxing effective, but unsustainable, non-pharmaceutical interventions (NPIs) need to be made. An individual- based transmission model of SARS-CoV-2 dynamics, OpenCOVID, was developed to compare the impact of various vaccination and NPI strategies on the COVID-19 epidemic in Switzerland. We estimate that any relaxation of NPIs in March 2021 will lead to increasing cases, hospitalisations, and deaths resulting in a ‘third wave’ in spring and into summer 2021. However, we find a cautious phased relaxation can substantially reduce population-level morbidity and mortality. We find that faster vaccination campaign can offset the size of such a wave, allowing more flexibility for NPI to be relaxed sooner. Our sensitivity analysis revealed that model results are particularly sensitive to the infectiousness of variant B.1.1.7.

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.

BackgroundMalaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host’s 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-naïve 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.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability. One Sentence Summary We propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

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.

BackgroundSubstantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. This study highlights the role of mathematical models to support intervention development.MethodsA mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. We demonstrate the power of our approach by application to five malaria interventions in development.ResultsAiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that the efficacy and duration needs depend on the biological action of the interventions. Interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements.ConclusionsOur approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

Ensuring continued success against malaria depends on a pipeline of new antimalarials. Antimalarial drug development utilizes preclinical murine and experimental human malaria infection studies to evaluate drug efficacy., Ensuring continued success against malaria depends on a pipeline of new antimalarials. Antimalarial drug development utilizes preclinical murine and experimental human malaria infection studies to evaluate drug efficacy. A sequential approach is typically adapted, with results from each stage informing the design of the next stage of development. The validity of this approach depends on confidence that results from murine malarial studies predict the outcome of clinical trials in humans. Parasite clearance rates following treatment are key parameters of drug efficacy. To investigate the validity of forward predictions, we developed a suite of mathematical models to capture parasite growth and drug clearance along the drug development pathway and estimated parasite clearance rates. When comparing the three infection experiments, we identified different relationships of parasite clearance with dose and different maximum parasite clearance rates. In Plasmodium berghei-NMRI mouse infections, we estimated a maximum parasite clearance rate of 0.2 (1/h); in Plasmodium falciparum-SCID mouse infections, 0.05 (1/h); and in human volunteer infection studies with P. falciparum, we found a maximum parasite clearance rate of 0.12 (1/h) and 0.18 (1/h) after treatment with OZ439 and MMV048, respectively. Sensitivity analysis revealed that host-parasite driven processes account for up to 25% of variance in parasite clearance for medium-high doses of antimalarials. Although there are limitations in translating parasite clearance rates across these experiments, they provide insight into characterizing key parameters of drug action and dose response and assist in decision-making regarding dosage for further drug development.

Tanzanian adult male volunteers were immunized by direct venous inoculation with radiation-attenuated, aseptic, purified, cryopreserved; Plasmodium falciparum; (Pf) sporozoites (PfSPZ Vaccine) and protective efficacy assessed by homologous controlled human malaria infection (CHMI). Serum immunoglobulin G (IgG) responses were analyzed longitudinally using a Pf protein microarray covering 91% of the proteome, providing first insights into naturally acquired and PfSPZ Vaccine-induced whole parasite antibody profiles in malaria pre-exposed Africans. Immunoreactivity was identified against 2239 functionally diverse Pf proteins, showing a wide breadth of humoral response. Antibody-based immune 'fingerprints' in these individuals indicated a strong person-specific immune response at baseline, with little changes in the overall humoral immunoreactivity pattern measured after immunization. The moderate increase in immunogenicity following immunization and the extensive and variable breadth of humoral immune response observed in the volunteers at baseline suggest that pre-exposure reduces vaccine-induced antigen reactivity in unanticipated ways.

Development of resistance to malaria treatments remains a great threat to continued malaria burden reduction and elimination. Quantifying the impact of key factors which increase the emergence and spread of drug resistance can guide intervention strategies. Whilst modelling provides a framework to understand these factors, we show that a simple of model with a sensitive-resistant dichotomy leads to incorrectly focusing on reducing the treatment rate as a means to prevent resistance. Instead we present a model that considers the development of resistance within hosts as a scale, and we then quantify the number of resistant infections that would arise from a single sensitive infection. By including just one step before full resistance, the model highlights that disrupting this development is more effective than reducing treatment rate. This result is compounded when the model includes the more realistic scenario of several intermediary steps. An additional comparison to transmission probabilities, where resistant infections are less likely to be transmitted (cost of resistance), confirms that preventing the establishment of resistance is more effective than controlling the spread. Our work strongly advocates for further studies into within-host models of resistance, including the potential of combination therapies to disrupt emergence.

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities.

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes. The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework. PBPK models for all ten drugs were developed in young adults (20–50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [Cmax], time to Cmax [tmax], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs. The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while Cmax, tmax and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter. The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Background Malaria was once a serious public health problem in China, with Plasmodium vivax the major species responsible for more than 90% of local transmission. Following significant integrated malaria control and elimination programmes, malaria burden declined, and since 2017 China has not recorded any indigenous case. To understand the historical malaria transmission patterns and epidemic characteristics in China and insights useful to guide P. vivax malaria control and elimination elsewhere, a retrospective study was carried out. Methods Historical data from a pilot study conducted in Guantang, Luyi in central China from 1971–1995, were digitized. The data included monthly numbers of reported cases, febrile cases, parasite carriage rates, the neonatal infection rate, and entomological data regarding Anopheles sinensis. Results Following 25 years of continuous integrated malaria control activities, malaria incidence in Guantang decreased from 4,333 cases per 10,000 in 1970 before integrated implementation to 0.23 cases per 10,000 in 1991, and no cases in 1992–1995. Some fluctuations in incidence were observed between 1977 and 1981. During the period parasite rates, antibody levels and the neonatal infection rate also decreased. The pattern of seasonality confirmed that P. vivax in Henan Province was primarily of the long incubation type (temperate) during non-transmission period. The findings retrospectively provide a scientific basis for the implementation of mass campaigns of liver stage hypnozoite clearance. Entomological studies indicated that An. sinensis was the only vector, and it preferred bovine to human hosts, predominantly biting and resting outdoors. Mosquito densities declined between 1971 and 1984. Conclusion The integrated malaria control approach in Guantang effectively controlled malaria and achieved elimination. Analysis of the effectiveness of the programme can provide guidance to other regions or countries with similar ecological settings aiming to move from malaria control to elimination. There is a potential challenge in the maintenance of non-transmission status owing to imported cases and the long dormancy of liver stage hypnozoites.

The critically important role of health for development was underlined in the 16th World Development Report entitled “Investing in health”, published in 1993 [1]. Put forth by the World Bank and enhanced with input from the World Health Organization (WHO), the report examined the interplay between human health, health policy, and economic development. In the period 2000-2015, health for development was strongly emphasized in the Millennium Development Goals (MDGs). Indeed, three of the eight MDGs explicitly featured health [2]. Meanwhile, major achievements have been made in population health. For instance, average global life expectancy has increased by more than 20 years between 1950 and 2010 [3]. Yet, there are areas of unfinished business, such as reducing child mortality and improving maternal health [4]. Key vulnerable groups, such as the poorest and most isolated populations, have been left excluded and marginalized [4,5]. In addition, there are new challenges, as for instance non-communicable diseases have surpassed infectious diseases in terms of global burden [6], novel infectious threats from zoonoses [7] and anti-microbial resistance [8] have emerged, there are toxic mixtures of chemicals compromising human, animal, and ecosystem health, while climate change, urbanization, and migration have amplified health problems and vulnerabilities [9]. Taken together, there are multifactorial stresses that ask for innovative, multi-partner, integrated approaches. ISSN:2047-2986

Tanzanian adult male volunteers were immunized by direct venous inoculation with radiation-attenuated, aseptic, purified, cryopreserved Plasmodium falciparum (Pf) sporozoites (PfSPZ Vaccine) and protective efficacy assessed by homologous controlled human malaria infection (CHMI). Serum immunoglobulin G (IgG) responses were analyzed longitudinally using a Pf protein microarray covering 91% of the proteome, providing first insights into naturally acquired and PfSPZ Vaccine-induced whole parasite antibody profiles in malaria pre-exposed Africans. Immunoreactivity was identified against 2239 functionally diverse Pf proteins, showing a wide breadth of humoral response. Antibody-based immune ‘fingerprints’ in these individuals indicated a strong person-specific immune response at baseline, with little changes in the overall humoral immunoreactivity pattern measured after immunization. The moderate increase in immunogenicity following immunization and the extensive and variable breadth of humoral immune response observed in the volunteers at baseline suggest that pre-exposure reduces vaccine-induced antigen reactivity in unanticipated ways.

Pelvic inflammatory disease (PID) is an infection of the upper genital tract occurring predominantly in sexually active young women. Chlamydia trachomatis and Neisseria gonorrhoeae are common causes; however, other cervical, enteric, bacterial vaginosis-associated, and respiratory pathogens, including Mycobacterium tuberculosis, may be involved. PID can be acute, chronic, or subclinical and is often underdiagnosed. Untreated PID can lead to chronic pelvic pain, infertility, ectopic pregnancy, and intra-abdominal infections. The diagnosis is made primarily on clinical suspicion, and empiric treatment is recommended in sexually active young women or women at risk for sexually transmitted infections who have unexplained lower abdominal or pelvic pain and cervical motion, uterine, or adnexal tenderness on examination. Mild to moderate disease can be treated in an outpatient setting with a single intramuscular injection of a recommended cephalosporin followed by oral doxycycline for 14 days. Additionally, metronidazole is recommended for 14 days in the setting of bacterial vaginosis, trichomoniasis, or recent uterine instrumentation. Hospitalization for parenteral antibiotics is recommended in patients who are pregnant or severely ill, in whom outpatient treatment has failed, those with tubo-ovarian abscess, or if surgical emergencies cannot be excluded. Treatment does not change in patients with intrauterine devices or those with HIV. Sex partner treatment is recommended; expedited partner treatment is recommended where legal. Prevention of PID includes screening for C. trachomatis and N. gonorrhoeae in all women younger than 25 years and those who are at risk or pregnant, plus intensive behavioral counseling for all adolescents and adults at increased risk of sexually transmitted infections.

Background Field studies are evaluating if mass drug administration (MDA) might shorten the time to elimination of Plasmodium falciparum malaria, when vector control measures and reactive surveillance strategies are scaled-up. A concern with this strategy is that there may be resurgence of transmission following MDA. Methods A conceptual model was developed to classify possible outcomes of an initial period of MDA, followed by continuously implementing other interventions. The classification considered whether elimination or a new endemic stable state is achieved, and whether changes are rapid, transient, or gradual. These categories were informed by stability analyses of simple models of vector control, case management, and test-and-treat interventions. Individual-based stochastic models of malaria transmission (OpenMalaria) were then used to estimate the probability and likely rates of resurgence in realistic settings. Effects of concurrent interventions, including routine case management and test-and-treat strategies were investigated. Results Analysis of the conceptual models suggest resurgence will occur after MDA unless transmission potential is very low, or the post-MDA prevalence falls below a threshold, which depends on both transmission potential and on the induction of bistability. Importation rates are important only when this threshold is very low. In most OpenMalaria simulations the approximately stable state achieved at the end of the simulations was independent of inclusion of MDA and the final state was unaffected by importation of infections at plausible rates. Elimination occurred only with high effective coverage of case management, low initial prevalence, and high intensity test-and-treat. High coverage of case management but not by test-and-treat induced bistability. Where resurgence occurred, its rate depended mainly on transmission potential (not treatment rates). Conclusions A short burst of high impact MDA is likely to be followed by resurgence. To avert resurgence, concomitant interventions need either to substantially reduce average transmission potential or to be differentially effective in averting or clearing infections at low prevalence. Case management at high effective coverage has this differential effect, and should suffice to avert resurgence caused by imported cases at plausible rates of importation. Once resurgence occurs, its rate depends mainly on transmission potential, not on treatment strategies.

There is a pressing need for alternative treatments against the liver fluke Opisthorchis viverrini . Oral tribendimidine is a promising candidate, but its population pharmacokinetic properties are unknown. Two phase IIa trials were conducted in Laos in O. viverrini -infected adults receiving single oral doses of 25 to 600 mg tribendimidine administered as different formulations in each study (study 1 used 200-mg tablets, and study 2 used 50-mg tablets). Venous whole blood, plasma, and capillary dried blood spots were sampled frequently from 68 adults, and concentrations of the tribendimidine metabolites dADT (deacetylated amidantel) and adADT (acetylated dADT) were measured. Population pharmacokinetics were assessed by using nonlinear mixed-effects modeling. The relationship between drug exposure and cure (assessed at 21 days posttreatment) was evaluated by using univariable logistic regression. A six-transit compartment absorption model with a one-disposition compartment for each metabolite described the data well. Compared to the 50-mg formulation (study 2), the 200-mg formulation (study 1) had a 40.1% higher mean transit absorption time, a 113% higher dADT volume of distribution, and a 364% higher adADT volume of distribution. Each 10-year increase in age was associated with a 12.7% lower dADT clearance and a 21.2% lower adADT clearance. The highest cure rates (≥55%) were observed with doses of ≥100 mg. Higher dADT, but not adADT, peak concentrations and exposures were associated with cure ( P = 0.004 and 0.003, respectively). For the first time, population pharmacokinetics of tribendimidine have been described. Known differences in the 200-mg versus 50-mg formulations were captured by covariate modeling. Further studies are needed to validate the structural model and confirm covariate relationships. (This study has been registered with the ISRCTN Registry under no. ISRCTN96948551.)

Aging is characterized by anatomical, physiological, and biological changes that can impact drug kinetics. The elderly are often excluded from clinical trials and knowledge about drug kinetics and drug-drug interaction magnitudes is sparse. Physiologically based pharmacokinetic modeling can overcome this clinical limitation but detailed descriptions of the population characteristics are essential to adequately inform models.; The objective of this study was to develop and verify a population database for aging Caucasians considering anatomical, physiological, and biological system parameters required to inform a physiologically based pharmacokinetic model that included population variability.; A structured literature search was performed to analyze age-dependent changes of system parameters. All collated data were carefully analyzed, and descriptive mathematical equations were derived.; A total of 362 studies were found of which 318 studies were included in the analysis as they reported rich data for anthropometric parameters and specific organs (e.g., liver). Continuous functions could be derived for most system parameters describing a Caucasian population from 20 to 99 years of age with variability. Areas with sparse data were identified such as tissue composition, but knowledge gaps were filled with plausible qualified assumptions. The developed population was implemented in Matlab; ®; and estimated system parameters from 1000 virtual individuals were in accordance with independent observed data showing the robustness of the developed population.; The developed repository for aging subjects provides a singular specific source for key system parameters needed for physiologically based pharmacokinetic modeling and can in turn be used to investigate drug kinetics and drug-drug interaction magnitudes in the elderly.