Assessing risks of Plasmodium falciparum resistance to select next-generation antimalarials

Abstract

Strategies to counteract or prevent emerging drug resistance are crucial for the design of next-generation antimalarials. In the past, resistant parasites were generally identified following treatment failures in patients, and compounds would have to be abandoned late in development. An early understanding of how candidate therapeutics lose efficacy as parasites evolve resistance is important to facilitate drug design and improve resistance detection and monitoring up to the postregistration phase. We describe a new strategy to assess resistance to antimalarial compounds as early as possible in preclinical development by leveraging tools to define the Plasmodium falciparum resistome, predict potential resistance risks of clinical failure for candidate therapeutics, and inform decisions to guide antimalarial drug development.

Keywords: Plasmodium falciparum; drug discovery; malaria; resistance.

Figure 1.. Life cycle of malaria parasites.

(A) Approximatively 20 minutes after human infection by Plasmodium sporozoites (top right), these forms reach the liver, infect hepatocytes and develop into liver schizonts over a ~5–7 day period, before bursting and liberating thousands of merozoites into the blood. Merozoites infect erythrocytes and develop successively into rings, trophozoites and schizonts that liberate merozoites (bottom right). At each asexual blood stage cycle, ~1% of trophozoites commit to producing sexual stage male and female gametocytes. Gametocytes differentiate in five successive stages, the first four being sequestered and the final one being liberated into the bloodstream. This stage V gametocyte can then be ingested by a mosquito during a blood meal. Once in the mosquito midgut, male and female gametocytes transform into gametes before mating and forming a diploid zygote. This zygote transforms into a motile ookinete that escapes the midgut contents and forms an oocyst under the basal lamina, wherein meiosis occurs leading the production of haploid sporozoites. Rupture of the mature oocyst liberates thousands of sporozoites (top left) that migrate to the mosquito salivary glands, ready to be injected into a human host during the next mosquito blood meal. Modified from Delves et al. [73]. (B) The three-dimensional aspect of this figure highlights differences in the parasite population size throughout the stages of the life cycle. At the liver stage fewer than 100 sporozoites reach the liver and only a few will successfully produce liver schizonts inside hepatocytes (b). At this stage the pressure of selection for mutated parasites leading to drug resistance is very low. Once the erythrocytic stage reaches its maximum after several proliferative cycles (a), the population of these asexual blood stage parasites can reach 10¹² in a patient. At this stage, the patient can develop severe malaria. This high number of parasites creates a high selective pressure for the emergence of resistance. Following differentiation into the non-proliferating gametocytes (c), the subsequent less abundant forms, i.e. micro- and macro-gametes (d), ookinetes (e), oocysts (f) and sporozoites (g), do not enable a high risk for the emergence of resistance. Modified from Leroy et al. [43].

Figure 2.. Resistance risk validation and evaluation, an iterative process between the bedside and the bench.

Learning from the clinical observations collected over the past two decades, our proposed new strategy for the evaluation of resistance risks relies on the “resistance triangle”. First, clinical outcomes, phenotypic studies and genotypic analyses are compiled from P. falciparum-infected patients and volunteers, through in vivo models, down to in vitro experiments. Resistance is declared when parasites from clinical recrudescences harbor validated resistant genetic markers and exhibit decreased in vitro sensitivity to the drug/compound. ACPR: adequate clinical and parasitological response; PCT: parasite clearance time; MIR: minimum inoculum for resistance; WGS: whole-genome sequencing; SNP: single nucleotide polymorphism; CNV: copy number variation; RSA: ring-survival assay, PSA: piperaquine survival assay.

Figure 3.. Multicriteria assessment of the risk of resistance and mitigation strategy.

Resistance is evaluated according to the stage of advancement in the pipeline of the compound/series. Early series (A) and Late-Lead compounds (B) are down-prioritized in the event of a MIR value ≤ 6 (i.e. resistance obtained in ≤ 10⁶ parasites), and an EC₅₀ fold shift > 10, generated in vitro in P. falciparum Dd2 parasites subjected to a selection pressure of 3×EC₉₀. Additionally, Late-Leads showing a cross-resistance greater than a 5-fold EC₅₀ shift compared with marketed antimalarials or advanced MMV portfolio compounds, or a median EC₅₀ with P. falciparum isolates 5-fold higher compared with values obtained with sensitive laboratory strains, will be down-prioritized.

Figure 4.. Quantitative assessment of the risk of resistance.

(A) The risk of resistance is assessed for each compound by integrating various in vitro (log₁₀MIR, EC₅₀ fold shift, resistance genotype and time of recrudescence in resistance selection experiments on at least two P. falciparum strains), in vivo (resistance-conferring mutation(s) in resistant recrudescing parasites from humanized mice efficacy studies) and ex-vivo (pre-existing resistance in the field) parameters. Each parameter is assigned a score corresponding to a high (red), medium (yellow) or low (green) risk, as depicted in parentheses. The weighting of the parameters varies according to their relevance. For instance, the day of recrudescence during in vitro selections was attributed a lower weight, to acknowledge the fact that whether this event occurs or not is more important than the timing. In contrast with the in vitro resistance genotype, where a clear distinction between SNPs and CNVs is made, both alterations are considered equally important in the in vivo studies. Indeed, while CNVs are more frequent in vitro, usually leading to small EC₅₀ shifts (i.e., < 5-fold), they can remain clinically relevant as evidenced with pfmdr1 copy number and mefloquine. Some data, particularly from in vivo studies, and the pre-existence of resistance conferring-mutations in the field, are not always available. To acknowledge the risk of such a knowledge gap and encourage teams to obtain these data, a medium score of 5 is attributed to the “not investigated” category. Publicly available information on genome sequence diversity is constantly increasing. While a thorough investigation at a given time point may reveal no concerns, updated information should be regularly considered for a given compound even after candidate declaration and during clinical development. Of note, some criteria, such as the EC₅₀ fold shift, the time of recrudescence, or the Log₁₀MIR value, may spread over two categories. In this case, we select the median score between the two categories. (B) Two complementary approaches are then used to classify compounds into a global risk zone: a numerical approach based on an additive score and a graphic assessment based on the relative area of a spider chart overlapping with the total high-risk area. A compound with a cumulative score (method A) up to 6 is classified in the low-risk zone, between 7 and 28 is in the medium-risk zone, and 29 or above is in the high-risk zone. A compound with an area (method B) covering up to 1% of the high-risk area is classified in the low-risk zone, between 1 and 26% is in the medium-risk zone, and above 26% is in the high-risk zone. As shown in Supplementary Figure 1, both approaches lead to the same outcome.