Malarial parasites vs. antimalarials: never-ending rumble in the jungle

Development of resistance is an increasing problem for antimalarial chemotherapy because resistance against most available drugs has developed in the majority of world-wide parasite populations. Therefore, several strategies to counteract resistance-development are in place. From the pharmaceutical side, identification of new targets and compounds, development of structural relatives of known antimalarials, and fixed combination therapy are pursued. On the other hand, clinical studies focus on novel regimens, distribution schemes and drug combinations. A third possibility to diminish progression of resistance is the application of evolutionary concepts to design new strategies for validation, monitoring and interference with the selection-process that leads to the spread of multidrug-resistance. Since the pharmacologic and clinical side of antimalarial chemotherapy is covered by recent reviews we refer to the newest developments only and lay our focus on determinants of selection for drug resistance in human malaria.     

Antimalarial drugs inhibiting hemozoin (beta-hematin) formation: a mechanistic update

Digestion of hemoglobin in the food vacuole of the malaria parasite produces very high quantities of redox active toxic free heme. Hemozoin (beta-hematin) formation is a unique process adopted by Plasmodium sp. to detoxify free heme. Hemozoin formation is a validated target for most of the well-known existing antimalarial drugs and considered to be a suitable target to develop new antimalarials. Here we discuss the possible mechanisms of free heme detoxification in the malaria parasite and the mechanistic details of compounds, which offer antimalarial activity by inhibiting hemozoin formation. The chemical nature of new antimalarial compounds showing antimalarial activity through the inhibition of hemozoin formation has also been incorporated, which may help to design future antimalarials with therapeutic potential against multi-drug resistant malaria.     

Discovery of New Compounds Active against Plasmodium falciparum by High Throughput Screening of Microbial Natural Products

Due to the low structural diversity within the set of antimalarial drugs currently available in the clinic and the increasing number of cases of resistance, there is an urgent need to find new compounds with novel modes of action to treat the disease. Microbial natural products are characterized by their large diversity provided in terms of the chemical complexity of the compounds and the novelty of structures. Microbial natural products extracts have been underexplored in the search for new antiparasitic drugs and even more so in the discovery of new antimalarials. Our objective was to find new druggable natural products with antimalarial properties from the MEDINA natural products collection, one of the largest natural product libraries harboring more than 130,000 microbial extracts. In this work, we describe the optimization process and the results of a phenotypic high throughput screen (HTS) based on measurements of Plasmodium lactate dehydrogenase. A subset of more than 20,000 extracts from the MEDINA microbial products collection has been explored, leading to the discovery of 3 new compounds with antimalarial activity. In addition, we report on the novel antiplasmodial activity of 4 previously described natural products.     

A New In Vivo Screening Paradigm to Accelerate Antimalarial Drug Discovery

The emergence of resistance to available antimalarials requires the urgent development of new medicines. The recent disclosure of several thousand compounds active in vitro against the erythrocyte stage of Plasmodium falciparum has been a major breakthrough, though converting these hits into new medicines challenges current strategies. A new in vivo screening concept was evaluated as a strategy to increase the speed and efficiency of drug discovery projects in malaria. The new in vivo screening concept was developed based on human disease parameters, i.e. parasitemia in the peripheral blood of patients on hospital admission and parasite reduction ratio (PRR), which were allometrically down-scaled into P. berghei-infected mice. Mice with an initial parasitemia (P₀) of 1.5% were treated orally for two consecutive days and parasitemia measured 24 h after the second dose. The assay was optimized for detection of compounds able to stop parasite replication (PRR = 1) or induce parasite clearance (PRR >1) with statistical power >99% using only two mice per experimental group. In the P. berghei in vivo screening assay, the PRR of a set of eleven antimalarials with different mechanisms of action correlated with human-equivalent data. Subsequently, 590 compounds from the Tres Cantos Antimalarial Set with activity in vitro against P. falciparum were tested at 50 mg/kg (orally) in an assay format that allowed the evaluation of hundreds of compounds per month. The rate of compounds with detectable efficacy was 11.2% and about one third of active compounds showed in vivo efficacy comparable with the most potent antimalarials used clinically. High-throughput, high-content in vivo screening could rapidly select new compounds, dramatically speeding up the discovery of new antimalarial medicines. A global multilateral collaborative project aimed at screening the significant chemical diversity within the antimalarial in vitro hits described in the literature is a feasible task.     

Parallel cascade identification as a means for automatically classifying protein sequences into structure/function groups

Current methods for automatically classifying protein sequences into structure/function groups, based on their hydrophobicity profiles, have typically required large training sets. The most successful of these methods are based on hidden Markov models, but may require hundreds of exemplars for training in order to obtain consistent results. In this paper, we describe a new approach, based on nonlinear system identification, which appears to require little training data to achieve highly promising results. Received: 16 March 1998 / Accepted in revised form: 2 June 1999     

Exploring natural microbial resources for the discovery of anti-malarial compounds

Microorganisms in nature are highly diverse biological resources, which can be explored for drug discovery. Some countries including Brazil, Columbia, Indonesia, China, and Mexico, which are blessed with geographical uniqueness with diverse climates and display remarkable megabiodiversity, potentially provide microorganismal resources for such exploitation. In this review, as an example of drug discovery campaigns against tropical parasitic diseases utilizing microorganisms from such a megabiodiversity country, we summarize our past and on-going activities toward discovery of new antimalarials. The program was held in a bilateral collaboration between multiple Indonesian and Japanese research groups. In order to develop a new platform of drug discovery utilizing Indonesian bioresources under an international collaborative scheme, we aimed at: 1) establishment of an Indonesian microbial depository, 2) development of robust enzyme-based and cell-based screening systems, and 3) technology transfer necessary for screening, purification, and identification of antimalarial compounds from microbial culture broths. We collected, characterized, and deposited Indonesian microbes. We morphologically and genetically characterized fungi and actinomycetes strains isolated from 5 different locations representing 3 Indonesian geographical areas, and validated genetic diversity of microbes. Enzyme-based screening was developed against two validated mitochondrial enzymes from Plasmodium falciparum, dihydroorotate dehydrogenase and malate:quinone oxidoreductase, while cell-based proliferation assay was developed using the erythrocytic stage parasite of 3D7 strain. More than 17 thousands microbial culture extracts were subjected to the enzyme- and cell-based screening. Representative anti-malarial compounds discovered in this campaign are discussed, including a few isolated compounds that have been identified for the first time as anti-malarial compounds. Our antimalarial discovery campaign validated the Indonesian microbial library as a powerful resource for drug discovery. We also discuss critical needs for selection criteria for hits at each stage of screening and hit deconvolution such as preliminary extraction test for the initial profiling of the active compounds and dereplication techniques to minimize repetitive discovery of known compounds.     

A New Set of Chemical Starting Points with Plasmodium falciparum Transmission-Blocking Potential for Antimalarial Drug Discovery

The discovery of new antimalarials with transmission blocking activity remains a key issue in efforts to control malaria and eventually eradicate the disease. Recently, high-throughput screening (HTS) assays have been successfully applied to Plasmodium falciparum asexual stages to screen millions of compounds, with the identification of thousands of new active molecules, some of which are already in clinical phases. The same approach has now been applied to identify compounds that are active against P. falciparum gametocytes, the parasite stage responsible for transmission. This study reports screening results for the Tres Cantos Antimalarial Set (TCAMS), of approximately 13,533 molecules, against P. falciparum stage V gametocytes. Secondary confirmation and cytotoxicity assays led to the identification of 98 selective molecules with dual activity against gametocytes and asexual stages. Hit compounds were chemically clustered and analyzed for appropriate physicochemical properties. The TCAMS chemical space around the prioritized hits was also studied. A selection of hit compounds was assessed ex vivo in the standard membrane feeding assay and demonstrated complete block in transmission. As a result of this effort, new chemical structures not connected to previously described antimalarials have been identified. This new set of compounds may serve as starting points for future drug discovery programs as well as tool compounds for identifying new modes of action involved in malaria transmission.     

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.Subject terms: Quantitative trait, Genetic models     

Antimalarial activity of compounds comprising a primary benzene sulfonamide fragment

Despite the urgent need for effective antimalarial drugs with novel modes of action no new chemical class of antimalarial drug has been approved for use since 1996. To address this, we have used a rational approach to investigate compounds comprising the primary benzene sulfonamide fragment as a potential new antimalarial chemotype. We report the in vitro activity against Plasmodium falciparum drug sensitive (3D7) and resistant (Dd2) parasites for a panel of fourteen primary benzene sulfonamide compounds. Our findings provide a platform to support the further evaluation of primary benzene sulfonamides as a new antimalarial chemotype, including the identification of the target of these compounds in the parasite.     

The antimalarial effect of iron chelators: studies in animal models and in humans with mild falciparum malaria.

In this study we explore the antimalarial effects of 3-hydroxypyridin-4-ones (CP compounds), a family of bidentate orally effective iron chelators in experimental animal systems in vivo and in vitro, and examine whether the iron chelator deferoxamine (DF) is active against human infection with P. falciparum. There was direct relation between lipid solubility of the CP compounds, which would facilitate membrane transit, and their in vivo antimalarial action, suggesting direct intracellular iron chelation as the most likely explantation for the antimalarial effect of iron chelators. Results of the double-blind, placebo controlled trial of DF in humans with asymptomatic parasitemia provided unequivocal evidence that this iron-chelating agent has antimalarial activity. Depriving the parasite of a metabolically important source of iron may represent a novel approach to antimalarial drug development. DF is a relatively ineffective intraerythrocytic chelator, and our data indicate that other orally effective iron chelators may have superior antimalarial activity in vivo. A systematic screening of available iron chelating drugs may result in the identification of potentially useful antimalarial compounds.     

Optimal breeding-value prediction using a sparse selection index

Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP.     

Performance of several variable-selection methods applied to real ecological data

I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression. Stepwise methods performed similarly to exhaustive algorithms for subset selection, and the choice of criterion for comparing models (Akaike's information criterion, Schwarz's Bayesian information criterion or F statistics) had little effect on predictive ability. For most of the data sets, two methods based on regression trees yielded models with substantially lower predictive ability. I argue that there is no 'best' method of variable selection and that any of the regression-based approaches discussed here is capable of yielding useful predictive models.     

New tyrosinase inhibitors selected by atomic linear indices-based classification models

In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds.     

Estimating the distribution of selection coefficients from phylogenetic data with applications to mitochondrial and viral DNA

The distribution of selection coefficients of new mutations is of key interest in population genetics. In this paper we explore how codon-based likelihood models can be used to estimate the distribution of selection coefficients of new amino acid replacement mutations from phylogenetic data. To obtain such estimates we assume that all mutations at the same site have the same selection coefficient. We first estimate the distribution of selection coefficients from two large viral data sets under the assumption that the viral population size is the same along all lineages of the phylogeny and that the selection coefficients vary among sites. We then implement several new models in which the lineages of the phylogeny may have different population sizes. We apply the new models to a data set consisting of the coding regions from eight primate mitochondrial genomes. The results suggest that there might be little power to determine the exact shape of the distribution of selection coefficient but that the normal and gamma distributions fit the data significantly better than the exponential distribution.     

Drug discovery for malaria: a very challenging and timely endeavor

The prevalence of resistance to known antimalarial drugs has resulted in the expansion of antimalarial drug discovery efforts. Academic and nonprofit institutions are partnering with the pharmaceutical industry to develop new antimalarial drugs. Several new antimalarial agents are undergoing clinical trials, mainly those resurrected from previous antimalarial drug discovery programs. Novel antimalarials are being advanced through the drug development process, of course, with the anticipated high failure rate typical of drug discovery. Many of these are summarized in this review. Mechanisms for funding antimalarial drug discovery and genomic information to aid drug target selection have never been better. It remains to be seen whether ongoing efforts will be sufficient for reducing malaria burden in the developing world.