Incorporating Stage-Specific Drug Action into Pharmacological Modeling of Antimalarial Drug Treatment

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure-treatment properties, biological factors, transmission intensity, and access to treatment-obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

Parasite Viability as a Measure of In Vivo Drug Activity in Preclinical and Early Clinical Antimalarial Drug Assessment

The rate at which parasitemia declines in a host after treatment with an antimalarial drug is a major metric for assessment of antimalarial drug activity in preclinical models and in early clinical trials. However, this metric does not distinguish between viable and nonviable parasites. Thus, enumeration of parasites may result in underestimation of drug activity for some compounds, potentially confounding its use as a metric for assessing antimalarial activity in vivo. Here, we report a study of the effect of artesunate on Plasmodium falciparum viability in humans and in mice. We first measured the drug effect in mice by estimating the decrease in parasite viability after treatment using two independent approaches to estimate viability. We demonstrate that, as previously reported in humans, parasite viability declines much faster after artesunate treatment than does the decline in parasitemia (termed parasite clearance). We also observed that artesunate kills parasites faster at higher concentrations, which is not discernible from the traditional parasite clearance curve and that each subsequent dose of artesunate maintains its killing effect. Furthermore, based on measures of parasite viability, we could accurately predict the in vivo recrudescence of infection. Finally, using pharmacometrics modeling, we show that the apparent differences in the antimalarial activity of artesunate in mice and humans are partly explained by differences in host removal of dead parasites in the two hosts. However, these differences, along with different pharmacokinetic profiles, do not fully account for the differences in activity. (This study has been registered with the Australian New Zealand Clinical Trials Registry under identifier ACTRN12617001394336.)

Title: A Comprehensive Review on Classifying Fast-acting and Slow-acting Antimalarial Agents Based on Time of Action and Target Organelle of Plasmodium sp

The clinical resistance towards malarial parasites has rendered many antimalarials ineffective, likely due to a lack of understanding of time of action and stage specificity of all life stages. Therefore, to tackle this problem a more incisive comprehensive analysis of the fast and slow-acting profile of antimalarial agents relating to parasite time-kill kinetics and the target organelle on the progression of blood-stage parasites was carried out. It is evident from numerous findings that drugs targeting food vacuole, nuclear components, and endoplasmic reticulum mainly exhibit a fast-killing phenotype within 24h affecting first-cycle activity. Whereas drugs targeting mitochondria, apicoplast, microtubules, parasite invasion and egress exhibit a largely slow-killing phenotype within 96-120h, affecting second-cycle activity with few exemptions as moderately fast-killing. It is essential to understand the susceptibility of drugs on rings, trophozoites, schizonts, merozoites, and the appearance of organelle at each stage of 48h intraerythrocytic parasite cycle. Therefore, these parameters may facilitate the paradigm for understanding the timing of antimalarials action in deciphering its precise mechanism linked with time. Thus, classifying drugs based on the time of killing may promote designing new combination regimens against varied strains of P. falciparum and evaluating potential clinical resistance.

Optimizing bulk segregant analysis of drug resistance using Plasmodium falciparum genetic crosses conducted in humanized mice

Classical malaria parasite genetic crosses involve isolation, genotyping, and phenotyping of progeny parasites, which is time consuming and laborious. We tested a rapid alternative approach-bulk segregant analysis (BSA)-that utilizes sequencing of bulk progeny populations with and without drug selection for rapid identification of drug resistance loci. We used dihydroartemisinin (DHA) selection in two genetic crosses and investigated how synchronization, cryopreservation, and the drug selection regimen impacted BSA success. We detected a robust quantitative trait locus (QTL) at kelch13 in both crosses but did not detect QTLs at four other candidate loci. QTLs were detected using synchronized, but not unsynchronized progeny pools, consistent with the stage-specific action of DHA. We also successfully applied BSA to cryopreserved progeny pools, expanding the utility of this approach. We conclude that BSA provides a powerful approach for investigating the genetic architecture of drug resistance in Plasmodium falciparum.

Optimal control analysis of hepatocytic-erythrocytic dynamics of Plasmodium falciparum malaria

This paper presents an in-host malaria model subject to anti-malarial drug treatment and malaria vaccine antigens combinations. Pontryagin's Maximum Principle is applied to establish optimal control strategies against infected erythrocytes, infected hepatocytes and malaria parasites. Results from numerical simulation reveal that a combination of pre-erythrocytic vaccine antigen, blood schizontocide and gametocytocide drugs would offer the best strategy to eradicate clinical P. falciparum malaria. Sensitivity analysis, further reveal that the efficacy of blood schizontocides and blood stage vaccines are crucial in the control of clinical malaria infection. Futhermore, we found that an effective blood schizontocide should be used alongside efficacious blood stage vaccine for rapid eradication of infective malaria parasites. The authors hope that the results of this study will help accelerate malaria elimination efforts by combining malaria vaccines and anti-malarial drugs against the deadly P. falciparum malaria.

Seeking an optimal dosing regimen for OZ439/DSM265 combination therapy for treating uncomplicated falciparum malaria

BACKGROUND

The efficacy of artemisinin-based combination therapies (ACTs), the first-line treatments for uncomplicated falciparum malaria, has been declining in malaria-endemic countries due to the emergence of malaria parasites resistant to these compounds. Novel alternative therapies are needed urgently to prevent the likely surge in morbidity and mortality due to failing ACTs.

OBJECTIVES

This study investigates the efficacy of the combination of two novel drugs, OZ439 and DSM265, using a biologically informed within-host mathematical model.

METHODS

A within-host model was developed, which accounts for the differential killing of these compounds against different stages of the parasite's life cycle and accommodates the pharmacodynamic interaction between the drugs. Data of healthy volunteers infected with falciparum malaria collected from four trials (three that administered OZ439 and DSM265 alone, and the fourth a combination of OZ439 and DSM265) were analysed. Model parameters were estimated in a hierarchical Bayesian framework.

RESULTS

The posterior predictive simulations of our model predicted that 800 mg of OZ439 combined with 450 mg of DSM265, which are within the safe and tolerable dose range, can provide above 90% cure rates 42 days after drug administration.

CONCLUSIONS

Our results show that the combination of OZ439 and DSM265 can be a promising alternative to replace ACTs. Our model can be used to inform future Phase 2 and 3 clinical trials of OZ439/DSM265, fast-tracking the deployment of this combination therapy in the regions where ACTs are failing. The dosing regimens that are shown to be efficacious and within safe and tolerable limits are suggested for future investigations.

Non-invasive monitoring of drug action: A new live in vitro assay design for Chagas' disease drug discovery

New assay designs are needed to improve the predictive value of the Trypanosoma cruzi in vitro tests used as part of the Chagas' disease drug development pipeline. Here, we employed a green fluorescent protein (eGFP)-expressing parasite line and live high-content imaging to monitor the growth of T. cruzi amastigotes in mouse embryonic fibroblasts. A novel assay design allowed us to follow parasite numbers over 6 days, in four-hour intervals, while occupying the microscope for only 24 hours per biological replicate. Dose-response curves were calculated for each time point after addition of test compounds, revealing how EC50 values first decreased over the time of drug exposure, and then leveled off. However, we observed that parasite numbers could vary, even in the untreated controls, and at different sites in the same well, which caused variability in the EC50 values. To overcome this, we established that fold change in parasite number per hour is a more robust and informative measure of drug activity. This was calculated based on an exponential growth model for every biological sample. The net fold change per hour is the result of parasite replication, differentiation, and death. The calculation of this fold change enabled us to determine the tipping point of drug action, i.e. the time point when the death rate of the parasites exceeded the growth rate and the fold change dropped below 1, depending on the drug concentration and exposure time. This revealed specific pharmacodynamic profiles of the benchmark drugs benznidazole and posaconazole.

Chagas' disease, caused by Trypanosoma cruzi, is a chronic debilitating infection occurring mostly in Latin America. There is an urgent need for new, well tolerated drugs. However, the latest therapeutic candidates have yielded disappointing outcomes in clinical trials, despite promising preclinical results. This demands new and more predictive in vitro assays. To address this, we have developed an assay design that enables the growth of T. cruzi intracellular forms to be monitored in real time, under drug pressure, for 6 days post-infection. This allowed us to establish the tipping point of drug action, when the death rate of the parasites exceeded the growth rate. The resulting pharmacodynamics profiles can provide robust and informative details on anti-chagasic candidates, as demonstrated for the benchmark drugs benznidazole and posaconazole.

Whole-Cell Phenotypic Screening of Medicines for Malaria Venture Pathogen Box Identifies Specific Inhibitors of Plasmodium falciparum Late-Stage Development and Egress

We report a systematic, cellular phenotype-based antimalarial screening of the Medicines for Malaria Venture Pathogen Box collection, which facilitated the identification of specific blockers of late-stage intraerythrocytic development of Plasmodium falciparum First, from standard growth inhibition assays, we identified 173 molecules with antimalarial activity (50% effective concentration [EC₅₀] ≤ 10 μM), which included 62 additional molecules over previously known antimalarial candidates from the Pathogen Box. We identified 90 molecules with EC₅₀ of ≤1 μM, which had significant effect on the ring-trophozoite transition, while 9 molecules inhibited the trophozoite-schizont transition and 21 molecules inhibited the schizont-ring transition (with ≥50% parasites failing to proceed to the next stage) at 1 μM. We therefore rescreened all 173 molecules and validated hits in microscopy to prioritize 12 hits as selective blockers of the schizont-ring transition. Seven of these molecules inhibited the calcium ionophore-induced egress of Toxoplasma gondii, a related apicomplexan parasite, suggesting that the inhibitors may be acting via a conserved mechanism which could be further exploited for target identification studies. We demonstrate that two molecules, MMV020670 and MMV026356, identified as schizont inhibitors in our screens, induce the fragmentation of DNA in merozoites, thereby impairing their ability to egress and invade. Further mechanistic studies would facilitate the therapeutic exploitation of these molecules as broadly active inhibitors targeting late-stage development and egress of apicomplexan parasites relevant to human health.

Quantum Chemical Lipophilicities of Antimalarial Drugs in Relation to Terminal Half-Life

According to the WHO, artemisinin-based combination therapies (ACTs) have been integral to the recent reduction in deaths due to Plasmodium falciparum malaria. ACT-resistant strains are an emerging problem and have evolved altered developmental stages, reducing exposure of the most susceptible stages to artemisinin drugs in popular ACTs. Lipophilicity, log K ow, is a guide in understanding and predicting pharmacokinetic properties such as terminal half-life which alters drug exposure. Consistent log K ow values are not necessarily available for artemisinin derivatives designed to extend terminal half-life, increase bioavailability, and reduce neurotoxicity. For other drugs used in ACTs, an assortment of experimental and computational log K ow values are available in the literature and in some cases, do not account for subtle but important differences between closely related structures such as between diastereomers. Quantum chemical methods such as density functional theory (DFT) used with an implicit solvent model allow for consistent comparison of physical properties including log K ow and distinguish between closely related structures. To this end, DFT, B3LYP/6-31G(d), with an implicit solvent model (SMD) was used to compute ΔG ow o and ΔG vow o for 1-octanol-water and olive oil-water partitions, respectively, for 21 antimalarial drugs: 12 artemisinin-based, 4 4-aminoquinolines and structurally similar pyronaridine, and 4 amino alcohols. The computed ΔG ow o was close to ΔG ow o calculated from experimental log K ow values from the literature where available, with a mean signed error of 2.3 kJ/mol and mean unsigned error of 3.7 kJ/mol. The results allow assignment of log K ow for α-and β-diastereomers of arteether, and prediction of log K ow for β-DHA and five experimental drugs. Linear least square analysis of log K ow and log K vow versus terminal elimination half-life showed strong linear relationships, once the data points for the 4-aminoquinoline drugs, mefloquine and pyronaridine were found to follow their own linear relationship, which is consistent with their different plasma protein binding. The linear relationship between the computed log K vow and terminal elimination half-life was particularly strong, R 2 = 0.99 and F = 467, and can be interpreted in terms of a simple pharmacokinetic model. Terminal elimination half-life for β-DHA and four experimental artemisinin drugs were estimated based on this linear relationship between log K vow and terminal t 1/2. The computed log K ow and log K vow values for epimers α- and β-DHA and α and β-arteether provide physical data that may be helpful in understanding their different pharmacokinetics and activity based on their different molecular geometries. Relative solubility of quinine and quinidine are found to be sensitive to thermal corrections to enthalpy and to vibrational entropy and do not follow the general trend of longer terminal t 1/2 with greater predicted log K ow. Geometric relaxation of α- and β-DHA in solvent and inclusion of thermal correction for enthalpy and entropy results in correct prediction that α-DHA is favored in aqueous environments compared to β-DHA. Predictions made regarding experimental drugs have implications regarding their potential use in response to artemisinin drug-resistant strains.

Optimal Treatments for Severe Malaria and the Threat Posed by Artemisinin Resistance

Background

Standard treatment for severe malaria is with artesunate; patient survival in the 24 hours immediately posttreatment is the key objective. Clinical trials use clearance rates of circulating parasites as their clinical outcome, but the pathology of severe malaria is attributed primarily to noncirculating, sequestered, parasites, so there is a disconnect between existing clinical metrics and objectives.

Methods

We extend existing pharmacokinetic/pharmacodynamic modeling methods to simulate the treatment of 10000 patients with severe malaria and track the pathology caused by sequestered parasites.

Results

Our model recovered the clinical outcomes of existing studies (based on circulating parasites) and showed a “simplified” artesunate regimen was noninferior to the existing World Health Organization regimen across the patient population but resulted in worse outcomes in a subgroup of patients with infections clustered in early stages of the parasite life cycle. This same group of patients were extremely vulnerable to resistance emerging in parasite early ring stages.

Conclusions

We quantify patient outcomes in a manner appropriate for severe malaria with a flexible framework that allows future researchers to implement their beliefs about underlying pathology. We highlight with some urgency the threat posed to treatment of severe malaria by artemisinin resistance in parasite early ring stages.

Improving Methods for Analyzing Antimalarial Drug Efficacy Trials: Molecular Correction Based on Length-Polymorphic Markers msp-1, msp-2, and glurp

Drug efficacy trials monitor the continued efficacy of front-line drugs against falciparum malaria. Overestimating efficacy results in a country retaining a failing drug as first-line treatment with associated increases in morbidity and mortality, while underestimating drug effectiveness leads to removal of an effective treatment with substantial practical and economic implications.

Drug efficacy trials monitor the continued efficacy of front-line drugs against falciparum malaria. Overestimating efficacy results in a country retaining a failing drug as first-line treatment with associated increases in morbidity and mortality, while underestimating drug effectiveness leads to removal of an effective treatment with substantial practical and economic implications. Trials are challenging: they require long durations of follow-up to detect drug failures, and patients are frequently reinfected during that period. Molecular correction based on parasite genotypes distinguishes reinfections from drug failures to ensure the accuracy of failure rate estimates. Several molecular correction “algorithms” have been proposed, but which is most accurate and/or robust remains unknown. We used pharmacological modeling to simulate parasite dynamics and genetic signals that occur in patients enrolled in malaria drug clinical trials. We compared estimates of treatment failure obtained from a selection of proposed molecular correction algorithms against the known “true” failure rate in the model. Our findings are as follows. (i) Molecular correction is essential to avoid substantial overestimates of drug failure rates. (ii) The current WHO-recommended algorithm consistently underestimates the true failure rate. (iii) Newly proposed algorithms produce more accurate failure rate estimates; the most accurate algorithm depends on the choice of drug, trial follow-up length, and transmission intensity. (iv) Long durations of patient follow-up may be counterproductive; large numbers of new infections accumulate and may be misclassified, overestimating drug failure rate. (v) Our model was highly consistent with existing in vivo data. The current WHO-recommended method for molecular correction and analysis of clinical trials should be reevaluated and updated.

In Silico Investigation of the Decline in Clinical Efficacy of Artemisinin Combination Therapies Due to Increasing Artemisinin and Partner Drug Resistance

Antimalarial treatment currently relies on an artemisinin derivative and a longer-acting partner drug. With the emergence of resistance to the artemisinin derivatives and the potential pressure this exerts on the partner drugs, the impact of resistance to each drug on efficacy needs to be investigated. An in silico exploration of dihydroartemisinin-piperaquine and mefloquine-artesunate, two artemisinin-based combination therapies that are commonly used in Southeast Asia, was performed. The percentage of treatment failures was simulated from a within-host pharmacokinetic-pharmacodynamic (PKPD) model, assuming that parasites developed increasing levels of (i) artemisinin derivative resistance or (ii) concomitant resistance to both the artemisinin derivative and the partner drug. Because the exact nature of how resistant Plasmodium falciparum parasites respond to treatment is unknown, we examined the impact on treatment failure rates of artemisinin resistance that (i) reduced the maximal killing rate, (ii) increased the concentration of drug required for 50% killing, or (iii) shortened the window of parasite stages that were susceptible to artemisinin derivatives until the drugs had no effect on the ring stages. The loss of the ring-stage activity of the artemisinin derivative caused the greatest increase in the treatment failure rate, and this result held irrespective of whether partner drug resistance was assumed to be present or not. To capture the uncertainty regarding how artemisinin derivative and partner drug resistance affects the assumed concentration-killing effect relationship, a variety of changes to this relationship should be considered when using within-host PKPD models to simulate clinical outcomes to guide treatment strategies for resistant infections.

Investigating the Efficacy of Triple Artemisinin-Based Combination Therapies for Treating Plasmodium falciparum Malaria Patients Using Mathematical Modeling

The first line treatment for uncomplicated falciparum malaria is artemisinin-based combination therapy (ACT), which consists of an artemisinin derivative coadministered with a longer-acting partner drug. However, the spread of Plasmodium falciparum resistant to both artemisinin and its partner drugs poses a major global threat to malaria control activities. Novel strategies are needed to retard and reverse the spread of these resistant parasites. One such strategy is triple artemisinin-based combination therapy (TACT). We developed a mechanistic within-host mathematical model to investigate the efficacy of a TACT (dihydroartemisinin-piperaquine-mefloquine [DHA-PPQ-MQ]) for use in South-East Asia, where DHA and PPQ resistance are now increasingly prevalent. Comprehensive model simulations were used to explore the degree to which the underlying resistance influences the parasitological outcomes. The effect of MQ dosing on the efficacy of TACT was quantified at various degrees of DHA and PPQ resistance. To incorporate interactions between drugs, a novel model is presented for the combined effect of DHA-PPQ-MQ, which illustrates how the interactions can influence treatment efficacy. When combined with a standard regimen of DHA and PPQ, the administration of three 6.7-mg/kg doses of MQ was sufficient to achieve parasitological efficacy greater than that currently recommended by World Health Organization (WHO) guidelines. As a result, three 8.3-mg/kg doses of MQ, the current WHO-recommended dosing regimen for MQ, combined with DHA-PPQ, has the potential to produce high cure rates in regions where resistance to DHA-PPQ has emerged.

A Dynamic Stress Model Explains the Delayed Drug Effect in Artemisinin Treatment of Plasmodium falciparum

Artemisinin resistance constitutes a major threat to the continued success of control programs for malaria, particularly in light of developing resistance to partner drugs. Improving our understanding of how artemisinin-based drugs act and how resistance manifests is essential for the optimization of dosing regimens and the development of strategies to prolong the life span of current first-line treatment options. Recent short-drug-pulse in vitro experiments have shown that the parasite killing rate depends not only on drug concentration but also the exposure time, challenging the standard pharmacokinetic-pharmacodynamic (PK-PD) paradigm in which the killing rate depends only on drug concentration. Here, we introduce a dynamic stress model of parasite killing and show through application to 3D7 laboratory strain viability data that the inclusion of a time-dependent parasite stress response dramatically improves the model's explanatory power compared to that of a traditional PK-PD model. Our model demonstrates that the previously reported hypersensitivity of early-ring-stage parasites of the 3D7 strain to dihydroartemisinin compared to other parasite stages is due primarily to a faster development of stress rather than a higher maximum achievable killing rate. We also perform in vivo simulations using the dynamic stress model and demonstrate that the complex temporal features of artemisinin action observed in vitro have a significant impact on predictions for in vivo parasite clearance. Given the important role that PK-PD models play in the design of clinical trials for the evaluation of alternative drug dosing regimens, our novel model will contribute to the further development and improvement of antimalarial therapies.

Quantifying the pharmacology of antimalarial drug combination therapy

Most current antimalarial drugs are combinations of an artemisinin plus a ‘partner’ drug from another class, and are known as artemisinin-based combination therapies (ACTs). They are the frontline drugs in treating human malaria infections. They also have a public-health role as an essential component of recent, comprehensive scale-ups of malaria interventions and containment efforts conceived as part of longer term malaria elimination efforts. Recent reports that resistance has arisen to artemisinins has caused considerable concern. We investigate the likely impact of artemisinin resistance by quantifying the contribution artemisinins make to the overall therapeutic capacity of ACTs. We achieve this using a simple, easily understood, algebraic approach and by more sophisticated pharmacokinetic/pharmacodynamic analyses of drug action; the two approaches gave consistent results. Surprisingly, the artemisinin component typically makes a negligible contribution (≪0.0001%) to the therapeutic capacity of the most widely used ACTs and only starts to make a significant contribution to therapeutic outcome once resistance has started to evolve to the partner drugs. The main threat to antimalarial drug effectiveness and control comes from resistance evolving to the partner drugs. We therefore argue that public health policies be re-focussed to maximise the likely long-term effectiveness of the partner drugs.

OptiMal-PK: an internet-based, user-friendly interface for the mathematical-based design of optimized anti-malarial treatment regimens

Background

The search for highly effective anti-malarial therapies has gathered pace and recent years have seen a number of promising single and combined therapies reach the late stages of development. A key drug development challenge is the need for early assessment of the clinical utility of new drug leads as it is often unclear for developers whether efforts should be focused on efficacy or metabolic stability/exposure or indeed whether the continuation of iterative QSAR (quantitative structure–activity and relationships) cycles of medicinal chemistry and biological testing will translate to improved clinical efficacy. Pharmacokinetic and pharmacodynamic (PK/PD)-based measurements available from in vitro studies can be used for such clinical predictions. However, these predictions often require bespoke mathematical PK/PD modelling expertise and are normally performed after candidate development and, therefore, not during the pre-clinical development phase when such decisions need to be made.

Methods

An internet-based tool has been developed using STELLA^® software. The tool simulates multiple differential equations that describe anti-malarial PK/PD relationships where the user can easily input PK/PD parameters. The tool utilizes a simple stop-light system to indicate the efficacy of each combination of parameters. This tool, called OptiMal-PK, additionally allows for the investigation of the effect of drug combinations with known or custom compounds.

Results

The results of simulations obtained from OptiMal-PK were compared to a previously published and validated mathematical model on which this tool is based. The tool has also been used to simulate the PK/PD relationship for a number of existing anti-malarial drugs in single or combined treatment. Simulations were predictive of the published clinical parasitological clearance activities for these existing therapies.

Conclusions

OptiMal-PK is designed to be implemented by medicinal chemists and pharmacologists during the pre-clinical anti-malarial drug development phase to explore the impact of different PK/PD parameters upon the predicted clinical activity of any new compound. It can help investigators to identify which pharmacological features of a compound are most important to the clinical performance of a new chemical entity and how partner drugs could potentially improve the activity of existing therapies.