scholarly journals Identification of compounds active against quiescent artemisinin-resistant Plasmodium falciparum parasites via the quiescent-stage survival assay (QSA)

2020 ◽  
Vol 75 (10) ◽  
pp. 2826-2834
Author(s):  
Thibaud Reyser ◽  
Lucie Paloque ◽  
Manel Ouji ◽  
Michel Nguyen ◽  
Sandie Ménard ◽  
...  
Keyword(s):  
Artemisinin Resistance ◽  
Laboratory Strain ◽  
Parasite Clearance ◽  
Quiescent State ◽  
Field Isolates ◽  
Pharmacological Targets ◽  
Survival Assay ◽  
Quiescent Stage ◽  
Selection Of

Abstract Background Quiescence is an unconventional mechanism of Plasmodium survival, mediating artemisinin resistance. This phenomenon increases the risk of clinical failures following artemisinin-based combination therapies (ACTs) by slowing parasite clearance and allowing the selection of parasites resistant to partner drugs. Objectives To thwart this multiresistance, the quiescent state of artemisinin-resistant parasites must be taken into consideration from the very early stages of the drug discovery process. Methods We designed a novel phenotypic assay we have named the quiescent-stage survival assay (QSA) to assess the antiplasmodial activity of drugs on quiescent parasites. This assay was first validated on quiescent forms from different artemisinin-resistant parasite lines (laboratory strain and field isolates), using two reference drugs with different mechanisms of action: chloroquine and atovaquone. Furthermore, the efficacies of different partner drugs of artemisinins used in ACTs were investigated against both laboratory strains and field isolates from Cambodia. Results Our results highlight that because of the mechanism of quiescence and the respective pharmacological targets of drugs, drug efficacies on artemisinin-resistant parasites may be different between quiescent parasites and their proliferating forms. Conclusions These data confirm the high relevance of adding the chemosensitivity evaluation of quiescent parasites by the specific in vitro QSA to the antiplasmodial drug development process in the current worrisome context of artemisinin resistance.

scholarly journals Mutation in Plasmodium falciparum BTB/POZ domain of K13 protein confers artemisinin resistance

2021 ◽  
Author(s):  
Lucie Paloque ◽  
Romain Coppée ◽  
Barbara H. Stokes ◽  
Nina F. Gnädig ◽  
Karamoko Niaré ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
West African ◽  
Whole Genome Sequence ◽  
Drug Pressure ◽  
Synonymous Mutations ◽  
Survival Assay ◽  
One Year

Partial artemisinin resistance, defined in patients as a delayed parasite clearance following artemisinin-based treatment, is conferred by non-synonymous mutations in the Kelch beta-propeller domain of the Plasmodium falciparum k13 ( pfk13 ) gene. Here, we carried out in vitro selection over a one-year period on a West African P. falciparum strain isolated from Kolle (Mali) under a dose-escalating artemisinin regimen. After 18 cycles of sequential drug pressure, the selected parasites exhibited enhanced survival to dihydroartemisinin in the ring-stage survival assay (RSA 0-3h = 9.2%). Sanger and whole-genome sequence analyses identified the PfK13 P413A mutation, localized in the BTB/POZ domain, upstream of the propeller domain. This mutation was sufficient to confer in vitro artemisinin resistance when introduced into the PfK13 coding sequence of the parasite strain Dd2 by CRISPR/Cas9 gene editing. These results together with structural studies of the protein demonstrate that the propeller domain is not the sole in vitro mediator of PfK13-mediated artemisinin resistance, and highlight the importance of monitoring for mutations throughout PfK13.

scholarly journals Artemisinin resistance phenotypes and K13 inheritance in a Plasmodium falciparum cross and Aotus model

2018 ◽  
Vol 115 (49) ◽  
pp. 12513-12518 ◽  
Author(s):  
Juliana M. Sá ◽  
Sarah R. Kaslow ◽  
Michael A. Krause ◽  
Viviana A. Melendez-Muniz ◽  
Rebecca E. Salzman ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Wild Type ◽  
Infected Monkey ◽  
Allelic Substitution ◽  
Survival Assay ◽  
Mean Differences

Concerns about malaria parasite resistance to treatment with artemisinin drugs (ARTs) have grown with findings of prolonged parasite clearance t1/2s (>5 h) and their association with mutations in Plasmodium falciparum Kelch-propeller protein K13. Here, we describe a P. falciparum laboratory cross of K13 C580Y mutant with C580 wild-type parasites to investigate ART response phenotypes in vitro and in vivo. After genotyping >400 isolated progeny, we evaluated 20 recombinants in vitro: IC50 measurements of dihydroartemisinin were at similar low nanomolar levels for C580Y- and C580-type progeny (mean ratio, 1.00; 95% CI, 0.62–1.61), whereas, in a ring-stage survival assay, the C580Y-type progeny had 19.6-fold (95% CI, 9.76–39.2) higher average counts. In splenectomized Aotus monkeys treated with three daily doses of i.v. artesunate, t1/2 calculations by three different methods yielded mean differences of 0.01 h (95% CI, −3.66 to 3.67), 0.80 h (95% CI, −0.92 to 2.53), and 2.07 h (95% CI, 0.77–3.36) between C580Y and C580 infections. Incidences of recrudescence were 57% in C580Y (4 of 7) versus 70% in C580 (7 of 10) infections (−13% difference; 95% CI, −58% to 35%). Allelic substitution of C580 in a C580Y-containing progeny clone (76H10) yielded a transformant (76H10C580Rev) that, in an infected monkey, recrudesced regularly 13 times over 500 d. Frequent recrudescences of ART-treated P. falciparum infections occur with or without K13 mutations and emphasize the need for improved partner drugs to effectively eliminate the parasites that persist through the ART component of combination therapy.

scholarly journals Delayed Parasite Clearance after Treatment with Dihydroartemisinin-Piperaquine in Plasmodium falciparum Malaria Patients in Central Vietnam

2014 ◽  
Vol 58 (12) ◽  
pp. 7049-7055 ◽  
Author(s):  
Kamala Thriemer ◽  
Nguyen Van Hong ◽  
Anna Rosanas-Urgell ◽  
Bui Quang Phuc ◽  
Do Manh Ha ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Clearance Rate ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Clearance Time ◽  
Sensitivity Testing ◽  
Content Type ◽  
Parasite Clearance Time ◽  
Central Vietnam

ABSTRACTReduced susceptibility ofPlasmodium falciparumtoward artemisinin derivatives has been reported from the Thai-Cambodian and Thai-Myanmar borders. Following increasing reports from central Vietnam of delayed parasite clearance after treatment with dihydroartemisinin-piperaquine (DHA-PPQ), the current first-line treatment, we carried out a study on the efficacy of this treatment. Between September 2012 and February 2013, we conducted a 42-dayin vivoandin vitroefficacy study in Quang Nam Province. Treatment was directly observed, and blood samples were collected twice daily until parasite clearance. In addition, genotyping, quantitative PCR (qPCR), andin vitrosensitivity testing of isolates was performed. The primary endpoints were parasite clearance rate and time. The secondary endpoints included PCR-corrected and uncorrected cure rates, qPCR clearance profiles,in vitrosensitivity results (for chloroquine, dihydroartemisinin, and piperaquine), and genotyping for mutations in the Kelch 13 propeller domain. Out of 672 screened patients, 95 were recruited and 89 available for primary endpoint analyses. The median parasite clearance time (PCT) was 61.7 h (interquartile range [IQR], 47.6 to 83.2 h), and the median parasite clearance rate had a slope half-life of 6.2 h (IQR, 4.4 to 7.5 h). The PCR-corrected efficacy rates were estimated at 100% at day 28 and 97.7% (95% confidence interval, 91.2% to 99.4%) at day 42. At day 3, theP. falciparumprevalence by qPCR was 2.5 times higher than that by microscopy. The 50% inhibitory concentrations (IC50s) of isolates with delayed clearance times (≥72 h) were significantly higher than those with normal clearance times for all three drugs. Delayed parasite clearance (PCT, ≥72 h) was significantly higher among day 0 samples carrying the 543 mutant allele (47.8%) than those carrying the wild-type allele (1.8%;P= 0.048). In central Vietnam, the efficacy of DHA-PPQ is still satisfactory, but the parasite clearance time and rate are indicative of emerging artemisinin resistance. (This study has been registered at ClinicalTrials.gov under registration no. NCT01775592.)

scholarly journals Plasmodium falciparum Founder Populations in Western Cambodia Have Reduced Artemisinin SensitivityIn Vitro

2014 ◽  
Vol 58 (8) ◽  
pp. 4935-4937 ◽  
Author(s):  
Chanaki Amaratunga ◽  
Benoit Witkowski ◽  
Dalin Dek ◽  
Vorleak Try ◽  
Nimol Khim ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Parasite Clearance ◽  
Parasite Development ◽  
Ring Stage ◽  
Content Type ◽  
Short Course ◽  
Founder Populations ◽  
Admixed Population ◽  
Survival Assay

ABSTRACTReducedPlasmodium falciparumsensitivity to short-course artemisinin (ART) monotherapy manifests as a long parasite clearance half-life. We recently defined three parasite founder populations with long half-lives in Pursat, western Cambodia, where reduced ART sensitivity is prevalent. Using the ring-stage survival assay, we show that these founder populations have reduced ART sensitivityin vitroat the early ring stage of parasite development and that a genetically admixed population contains subsets of parasites with normal or reduced ART sensitivity.

scholarly journals Plasmodium falciparum K13 mutations in Africa and Asia present varying degrees of artemisinin resistance and an elevated fitness cost in African parasites

2021 ◽  
Author(s):  
Barbara H. Stokes ◽  
Kelly Rubiano ◽  
Satish K. Dhingra ◽  
Sachel Mok ◽  
Judith Straimer ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Southeast Asia ◽  
Survival Rates ◽  
Point Mutations ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Definitive Evidence ◽  
The Impact

AbstractThe emergence of artemisinin (ART) resistance in Plasmodium falciparum parasites has led to increasing rates of treatment failure with first-line ART-based combination therapies (ACTs) in Southeast Asia. In this region, select mutations in K13 can result in delayed parasite clearance rates in vivo and enhanced survival in the ring-stage survival assay (RSA) in vitro. Our genotyping of 3,299 P. falciparum isolates across 11 sub-Saharan countries reveals the continuing dominance of wild-type K13 and confirms the emergence of a K13 R561H variant in Rwanda. Using gene editing, we provide definitive evidence that this mutation, along with M579I and C580Y, can confer variable degrees of in vitro ART resistance in African P. falciparum strains. C580Y and M579I were both associated with substantial fitness costs in African parasites, which may counter-select against their dissemination in high-transmission settings. We also report the impact of multiple K13 mutations, including the predominant variant C580Y, on RSA survival rates and fitness in multiple Southeast Asian strains. No change in ART susceptibility was observed upon editing point mutations in ferrodoxin or mdr2, earlier associated with ART resistance in Southeast Asia. These data point to the lack of an evident biological barrier to mutant K13 mediating ART resistance in Africa, while identifying their detrimental impact on parasite growth.

scholarly journals Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 62
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Sub Saharan Africa ◽  
Ensemble Machine Learning ◽  
Sub Saharan ◽  
Underlying Mechanisms

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

scholarly journals The extended recovery ring-stage survival assay provides superior prediction of patient clearance half-life and increases throughput

2019 ◽  
Author(s):  
Sage Z. Davis ◽  
Puspendra P. Singh ◽  
Katelyn M. Vendrely ◽  
Douglas A. Shoue ◽  
Lisa A. Checkley ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Half Life ◽  
Artemisinin Resistance ◽  
Pcr Amplification ◽  
Fold Change ◽  
Ring Stage ◽  
Post Exposure ◽  
Survival Assay

Abstract Background Tracking and understanding artemisinin resistance is key for preventing global setbacks in malaria eradication efforts. The ring-stage survival assay (RSA) is the current gold standard for in vitro artemisinin resistance phenotyping. However, the RSA has several drawbacks: it is relatively low throughput, has high variance due to microscopy readout, and correlates poorly with the current benchmark for in vivo resistance, patient clearance half-life post-artemisinin treatment. Here a modified RSA is presented, the extended Recovery Ring-stage Survival Assay (eRRSA), using 15 cloned patient isolates from Southeast Asia with a range of patient clearance half-lives, including parasite isolates with and without kelch13 mutations. Methods P. falciparum cultures were synchronized with single layer Percoll during the schizont stage of the erythrocytic cycle. Cultures were left to reinvade to early ring-stage and parasitemia was quantified using flow cytometry. Cultures were diluted to 2% hematocrit and 0.5% parasitemia in a 96-well plate to start the assay, allowing for increased throughput and decreased variability between biological replicates. Parasites were treated with 700nM of dihydroartemisinin or an equivalent amount of dimethyl sulfoxide (DMSO) for 6 h, washed three times in drug-free media, and incubated for 66 or 114 h, when samples were collected and frozen for PCR amplification. A SYBR Green-based quantitative PCR method was used to quantify the fold-change between treated and untreated samples. Results 15 cloned patient isolates from Southeast Asia with a range of patient clearance half-lives were assayed using the eRRSA. Due to the large number of pyknotic and dying parasites at 66 h post-exposure (72 h sample), parasites were grown for an additional cell cycle (114 h post-exposure, 120 h sample), which drastically improved correlation with patient clearance half-life compared to the 66 h post-exposure sample. A Spearman correlation of 0.8393 between fold change and patient clearance half-life was identified in these 15 isolates from Southeast Asia, which is the strongest correlation reported to date. Conclusions eRRSA drastically increases the efficiency and accuracy of in vitro artemisinin resistance phenotyping compared to the traditional RSA, which paves the way for extensive in vitro phenotyping of hundreds of artemisinin resistant parasites.

scholarly journals Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 62
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Sub Saharan Africa ◽  
Ensemble Machine Learning ◽  
Sub Saharan ◽  
Underlying Mechanisms

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

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

2017 ◽  
Vol 61 (12) ◽  
Author(s):  
Pengxing Cao ◽  
Nectarios Klonis ◽  
Sophie Zaloumis ◽  
Con Dogovski ◽  
Stanley C. Xie ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Dynamic Stress ◽  
Explanatory Power ◽  
Artemisinin Resistance ◽  
Laboratory Strain ◽  
Stress Model ◽  
Killing Rate ◽  
Dosing Regimens

ABSTRACT 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.

scholarly journals Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria

2019 ◽  
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Final Model ◽  
Ensemble Machine Learning ◽  
Massively Parallel Processing ◽  
Underlying Mechanisms

ABSTRACTAntiparasitic resistance in malaria is a growing concern affecting many areas of the eastern world. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway.The 2019 Malaria Dream Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict Artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles.In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

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