Semimechanistic Pharmacokinetic and Pharmacodynamic Modeling of Piperaquine in a Volunteer Infection Study with Plasmodium falciparum Blood-Stage Malaria

Leishmania blood parasite dynamics during and after treatment of visceral leishmaniasis in Eastern Africa: A pharmacokinetic-pharmacodynamic model

BACKGROUND

With the current treatment options for visceral leishmaniasis (VL), recrudescence of the parasite is seen in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting patient relapse in cases of VL. This study aimed to characterize the kinetics of circulating Leishmania parasites in the blood, during and after different antileishmanial therapies, and to find predictors for clinical relapse of disease.

METHODS

Data from three clinical trials, in which Eastern African VL patients received various antileishmanial regimens, were combined in this study. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during, and up to six months after treatment. An integrated population pharmacokinetic-pharmacodynamic model was developed using non-linear mixed effects modelling.

RESULTS

Parasite proliferation was best described by an exponential growth model, with an in vivo parasite doubling time of 7.8 days (RSE 12%). Parasite killing by fexinidazole, liposomal amphotericin B, sodium stibogluconate, and miltefosine was best described by linear models directly relating drug concentrations to the parasite elimination rate. After treatment, parasite growth was assumed to be suppressed by the host immune system, described by an Emax model driven by the time after treatment. No predictors for the high variability in onset and magnitude of the immune response could be identified. Model-based individual predictions of blood parasite load on Day 28 and Day 56 after start of treatment were predictive for clinical relapse of disease.

CONCLUSION

This semi-mechanistic pharmacokinetic-pharmacodynamic model adequately captured the blood parasite dynamics during and after treatment, and revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could be a useful biomarker to assess treatment efficacy of a treatment regimen in a clinical trial setting.

The problem of antimalarial resistance and its implications for drug discovery

INTRODUCTION

Malaria remains a devastating infectious disease with hundreds of thousands of casualties each year. Antimalarial drug resistance has been a threat to malaria control and elimination for many decades and is still of concern today. Despite the continued effectiveness of current first-line treatments, namely artemisinin-based combination therapies, the emergence of drug-resistant parasites in Southeast Asia and even more alarmingly the occurrence of resistance mutations in Africa is of great concern and requires immediate attention.

AREAS COVERED

A comprehensive overview of the mechanisms underlying the acquisition of drug resistance in Plasmodium falciparum is given. Understanding these processes provides valuable insights that can be harnessed for the development and selection of novel antimalarials with reduced resistance potential. Additionally, strategies to mitigate resistance to antimalarial compounds on the short term by using approved drugs are discussed.

EXPERT OPINION

While employing strategies that utilize already approved drugs may offer a prompt and cost-effective approach to counter antimalarial drug resistance, it is crucial to recognize that only continuous efforts into the development of novel antimalarial drugs can ensure the successful treatment of malaria in the future. Incorporating resistance propensity assessment during this developmental process will increase the likelihood of effective and enduring malaria treatments.

Extraordinary parasite multiplication rates in human malaria infections

For pathogenic organisms, faster rates of multiplication promote transmission success, the potential to harm hosts, and the evolution of drug resistance. Parasite multiplication rates (PMRs) are often quantified in malaria infections, given the relative ease of sampling. Using modern and historical human infection data, we show that established methods return extraordinarily - and implausibly - large PMRs. We illustrate how inflated PMRs arise from two facets of malaria biology that are far from unique: (i) some developmental ages are easier to sample than others; (ii) the distribution of developmental ages changes over the course of infection. The difficulty of accurately quantifying PMRs demonstrates a need for robust methods and a subsequent re-evaluation of what is known even in the well-studied system of malaria.