High-throughput matrix screening identifies synergistic and antagonistic antimalarial drug combinations

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool

PURPOSE

Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations.

METHODS

The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2).

RESULTS

The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool.

CONCLUSION

 The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Comparative Assessment and High-Throughput Drug-Combination Profiling of TEAD-Palmitoylation Inhibitors in Hippo Pathway Deficient Mesothelioma

The hippo signaling pathway is a central tumor suppressor cascade frequently inactivated in selected human cancers, leading to the aberrant activation of TEAD transcription factors. Whereas several TEAD auto-palmitoylation inhibitors are currently in development, a comprehensive assessment of this novel drug-modality is missing. Here, we report a comparative analysis among six TEADi(s) using cell-based and biochemical assays in Hippo pathway deficient mesothelioma. Our analysis revealed varying potency and selectivity across TEADi, also highlighting their limited efficacy. To overcome this limitation, we performed an unbiased, quantitative high-throughput drug screening by combining the TEADi VT-103 with a library of approximately 3000 oncology-focused drugs. By exploiting this library's mechanistic redundancy, we identified several drug-classes robustly synergized with TEADi. These included glucocorticoid-receptor (GR) agonists, Mek1/2 inhibitors, mTOR inhibitors, and PI3K inhibitors, among others. Altogether, we report a coherent single-agent dataset informing on potency and selectivity of TEAD-palmitoylation inhibitors as single-agents. We also describe a rational pipeline enabling the systematic identification of TEAD druggable co-dependencies. This data should support the pre-clinical development of drug combination strategies for the treatment of Hippo-deficient mesothelioma, and more broadly, for other cancers dependent on the oncogenic activity of YAP/TEAD.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

Predicting Optimal Antimalarial Drug Combinations from a Standardized Plasmodium falciparum Humanized Mouse Model

The development of new combinations of antimalarial drugs is urgently needed to prevent the spread of parasites resistant to drugs in clinical use and contribute to the control and eradication of malaria. In this work, we evaluated a standardized humanized mouse model of erythrocyte asexual stages of Plasmodium falciparum (PfalcHuMouse) for the selection of optimal drug combinations. First, we showed that the replication of P. falciparum was robust and highly reproducible in the PfalcHuMouse model by retrospective analysis of historical data. Second, we compared the relative value of parasite clearance from blood, parasite regrowth after suboptimal treatment (recrudescence), and cure as variables of therapeutic response to measure the contributions of partner drugs to combinations in vivo. To address the comparison, we first formalized and validated the day of recrudescence (DoR) as a new variable and found that there was a log-linear relationship with the number of viable parasites per mouse. Then, using historical data on monotherapy and two small cohorts of PfalcHuMice evaluated with ferroquine plus artefenomel or piperaquine plus artefenomel, we found that only measurements of parasite killing (i.e., cure of mice) as a function of drug exposure in blood allowed direct estimation of the individual drug contribution to efficacy by using multivariate statistical modeling and intuitive graphic displays. Overall, the analysis of parasite killing in the PfalcHuMouse model is a unique and robust experimental in vivo tool to inform the selection of optimal combinations by pharmacometric pharmacokinetic and pharmacodynamic (PK/PD) modeling.

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Human Polo-like Kinase Inhibitors as Antiplasmodials

Protein kinases have proven to be a very productive class of therapeutic targets, and over 90 inhibitors are currently in clinical use primarily for the treatment of cancer. Repurposing these inhibitors as antimalarials could provide an accelerated path to drug development. In this study, we identified BI-2536, a known potent human polo-like kinase 1 inhibitor, with low nanomolar antiplasmodial activity. Screening of additional PLK1 inhibitors revealed further antiplasmodial candidates despite the lack of an obvious orthologue of PLKs in Plasmodium. A subset of these inhibitors was profiled for their in vitro killing profile, and commonalities between the killing rate and inhibition of nuclear replication were noted. A kinase panel screen identified PfNEK3 as a shared target of these PLK1 inhibitors; however, phosphoproteome analysis confirmed distinct signaling pathways were disrupted by two structurally distinct inhibitors, suggesting PfNEK3 may not be the sole target. Genomic analysis of BI-2536-resistant parasites revealed mutations in genes associated with the starvation-induced stress response, suggesting BI-2536 may also inhibit an aminoacyl-tRNA synthetase.

qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data

High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standard HTS data can be simply plotted as an x-y graph usually represented as % activity of a compound tested at a single concentration vs compound ID, whereas quantitative HTS (qHTS) data incorporates a third axis represented by concentration. By virtue of the additional data points arising from the compound titration and the incorporation of logistic fit parameters that define the concentration-response curve, such as EC50 and Hill slope, qHTS data has been challenging to display on a single graph. Here we provide a flexible solution to the rapid plotting of complete qHTS data sets to produce a 3-axis plot we call qHTS Waterfall Plots. The software described here can be generally applied to any 3-axis dataset and is available as both an R package and an R shiny application.

A Plasmodium falciparum RING Finger E3 Ubiquitin Ligase Modifies the Roles of PfMDR1 and PfCRT in Parasite Drug Responses

Protein ubiquitination is an important posttranslational regulation mechanism that mediates Plasmodium development and modifies parasite responses to antimalarial drugs. Although mutations in several parasite ubiquitination enzymes have been linked to increased drug tolerance, the molecular mechanisms by which ubiquitination pathways mediate these parasite responses remain largely unknown. Here, we investigate the roles of a Plasmodium falciparum ring finger ubiquitin ligase (PfRFUL) in parasite development and in responses to antimalarial drugs. We engineered a transgenic parasite having the Pfrful gene tagged with an HA-2A-NeoR-glmS sequence to knockdown (KD) Pfrful expression using glucosamine (GlcN). A Western blot analysis of the proteins from GlcN-treated pSLI-HA-NeoR-glmS-tagged (PfRFULg) parasites, relative to their wild-type (Dd2) controls, showed changes in the ubiquitination of numerous proteins. PfRFUL KD rendered the parasites more sensitive to multiple antimalarial drugs, including mefloquine, piperaquine, amodiaquine, and dihydroartemisinin. PfRFUL KD also decreased the protein level of the P. falciparum multiple drug resistance 1 protein (PfMDR1) and altered the ratio of two bands of the P. falciparum chloroquine resistance transporter (PfCRT), suggesting contributions to the changed drug responses by the altered ubiquitination of these two molecules. The inhibition of proteasomal protein degradation by epoxomicin increased the PfRFUL level, suggesting the degradation of PfRFUL by the proteasome pathways, whereas the inhibition of E3 ubiquitin ligase activities by JNJ26854165 reduced the PfRFUL level. This study reveals the potential mechanisms of PfRFUL in modifying the expression of drug transporters and their roles in parasite drug responses. PfRFUL could be a potential target for antimalarial drug development.

The anticancer human mTOR inhibitor sapanisertib potently inhibits multiple Plasmodium kinases and life cycle stages

Compounds acting on multiple targets are critical to combating antimalarial drug resistance. Here, we report that the human "mammalian target of rapamycin" (mTOR) inhibitor sapanisertib has potent prophylactic liver stage activity, in vitro and in vivo asexual blood stage (ABS) activity, and transmission-blocking activity against the protozoan parasite Plasmodium spp. Chemoproteomics studies revealed multiple potential Plasmodium kinase targets, and potent inhibition of Plasmodium phosphatidylinositol 4-kinase type III beta (PI4Kβ) and cyclic guanosine monophosphate-dependent protein kinase (PKG) was confirmed in vitro. Conditional knockdown of PI4Kβ in ABS cultures modulated parasite sensitivity to sapanisertib, and laboratory-generated P. falciparum sapanisertib resistance was mediated by mutations in PI4Kβ. Parasite metabolomic perturbation profiles associated with sapanisertib and other known PI4Kβ and/or PKG inhibitors revealed similarities and differences between chemotypes, potentially caused by sapanisertib targeting multiple parasite kinases. The multistage activity of sapanisertib and its in vivo antimalarial efficacy, coupled with potent inhibition of at least two promising drug targets, provides an opportunity to reposition this pyrazolopyrimidine for malaria.

A High-Throughput Screening Platform Identifies Novel Combination Treatments for Malignant Peripheral Nerve Sheath Tumors

Malignant peripheral nerve sheath tumors (MPNST) are soft-tissue sarcomas that are the leading cause of mortality in patients with Neurofibromatosis type 1 (NF1). Single chemotherapeutic agents have shown response rates ranging from 18% to 44% in clinical trials, so there is still a high medical need to identify chemotherapeutic combination treatments that improve clinical prognosis and outcome. We screened a collection of compounds from the NCATS Mechanism Interrogation PlatE (MIPE) library in three MPNST cell lines, using cell viability and apoptosis assays. We then tested whether compounds that were active as single agents were synergistic when screened as pairwise combinations. Synergistic combinations in vitro were further evaluated in patient-derived orthotopic xenograft/orthoxenograft (PDOX) athymic models engrafted with primary MPNST matching with their paired primary-derived cell line where synergism was observed. The high-throughput screening identified 21 synergistic combinations, from which four exhibited potent synergies in a broad panel of MPNST cell lines. One of the combinations, MK-1775 with Doxorubicin, significantly reduced tumor growth in a sporadic PDOX model (MPNST-SP-01; sevenfold) and in an NF1-PDOX model (MPNST-NF1-09; fourfold) and presented greater effects in TP53 mutated MPNST cell lines. The other three combinations, all involving Panobinostat (combined with NVP-BGT226, Torin 2, or Carfilzomib), did not reduce the tumor volume in vivo at noncytotoxic doses. Our results support the utility of our screening platform of in vitro and in vivo models to explore new therapeutic approaches for MPNSTs and identified that combination MK-1775 with Doxorubicin could be a good pharmacologic option for the treatment of these tumors.

CDCDB: A large and continuously updated drug combination database

In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book^®, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/.

Measurement(s)	drug combination effect modeling • drug combination effect modeling
Technology Type(s)	Text mining • Clinical Trials Informatics System
Factor Type(s)	Medicine
Sample Characteristic - Organism	Homo sapiens

GPC1-Targeted Immunotoxins Inhibit Pancreatic Tumor Growth in Mice via Depletion of Short-lived GPC1 and Downregulation of Wnt Signaling

Glypican-1 (GPC1) is a cell surface proteoglycan that is upregulated in multiple types of human cancers including pancreatic cancer. Here, we investigated whether GPC1 could be a target of antibody-toxin fusion proteins (i.e., immunotoxins) for treating pancreatic cancer. We constructed a panel of GPC1-targeted immunotoxins derived from a functional domain of Pseudomonas exotoxin A. An albumin-binding domain was also introduced into the anti-GPC1 immunotoxin to improve serum half-life. Small-molecule screening was performed to identify irinotecan that shows synergistic efficacy with the immunotoxin. We showed that GPC1 was internalized upon antibody binding. Anti-GPC1 immunotoxins alone inhibited tumor growth in a pancreatic cancer xenograft model. The immunotoxin treatment reduced active β-catenin expression in tumor cells. Furthermore, immunotoxins containing an albumin-binding domain in combination with irinotecan caused pancreatic tumor regression. GPC1 expression was reduced by the immunotoxin treatment due to the degradation of the internalized GPC1 and its short cellular turnover rate. Our data indicate that the GPC1-targeted immunotoxin inhibits pancreatic tumor growth via degradation of internalized GPC1, downregulation of Wnt signaling, and inhibition of protein synthesis. The anti-GPC1 immunotoxin in combination with irinotecan thus provides a potential new treatment strategy for patients with pancreatic tumors.

Ex vivo drug sensitivity screening in multiple myeloma identifies drug combinations that act synergistically

The management of multiple myeloma (MM) is challenging: an assortment of available drug combinations adds complexity to treatment selection, and treatment resistance frequently develops. Given the heterogeneous nature of MM, personalized testing tools are required to identify drug sensitivities. To identify drug sensitivities in MM cells, we established a drug testing pipeline to examine ex vivo drug responses. MM cells from 44 patients were screened against 30 clinically relevant single agents and 44 double and triple drug combinations. We observed variability in responses across samples. The presence of gain(1q21) was associated with low sensitivity to venetoclax, and decreased ex vivo responses to dexamethasone reflected the drug resistance observed in patients. Less heterogeneity and higher efficacy was detected with many combinations compared to the corresponding single agents. We identified new synergistic effects of melflufen plus panobinostat using low concentrations (0.1-10 nM and 8 nM, respectively). In agreement with clinical studies, clinically approved combinations, such as triple combination of selinexor plus bortezomib plus dexamethasone, acted synergistically, and synergies required low drug concentrations (0.1 nM bortezomib, 10 nM selinexor and 4 nM dexamethasone). In summary, our drug screening provided results within a clinically actionable 5-day time frame and identified synergistic drug efficacies in patient-derived MM cells that may aid future therapy choices.

Non-parametric synergy modeling of chemical compounds with Gaussian processes

Background

Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose–response relationship with a parametric model.

Results

We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. Statistical significance of the difference is assessed from the confidence intervals of the Gaussian process fits. We evaluate performance of our model on a simulated data set from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better on these data sets. We also compare our model to the MuSyC model as an example of a recent method on these five data sets and on two-drug combination screens: Mott et al. anti-malarial screen and O’Neil et al. anti-cancer screen. We identify cases in which the HandGP model is preferred and cases in which the MuSyC model is preferred.

Conclusion

The Hand-GP model is a flexible model to capture synergy. Its non-parametric and probabilistic nature allows it to model a wide variety of response patterns.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04508-7.

Solvent Structure and Dynamics near the Surfaces of β-Hematin Crystals

Hematin crystallization, which is an essential component of the physiology of malaria parasites and the most successful target for antimalarial drugs, proceeds in mixed organic-aqueous solvents both in vivo and in vitro. Here we employ molecular dynamics simulations to examine the structuring and dynamics of a water-normal octanol mixture (a solvent that mimics the environment hosting hematin crystallization in vivo) in the vicinity of the typical faces in the habit of a hematin crystal. The simulations reveal that the properties of the solvent in the layer adjacent to the crystal are strongly impacted by the distinct chemical and topological features presented by each crystal face. The solvent organizes into at least three distinct layers. We also show that structuring of the solvent near the different faces of β-hematin strongly impacts the interfacial dynamics. The relaxation time of n-octanol molecules is longest in the contact layers and correlates with the degree of structural ordering at the respective face. We show that the macroscopically homogeneous water-octanol solution holds clusters of water and n-octanol connected by hydrogen bonds that entrap the majority of the water but are mostly smaller than 30 water molecules. Near the crystal surface the clusters anchor on hematin carboxyl groups. These results provide a direct example that solvent structuring is not restricted to aqueous and other hydrogen-bonded solutions. Our findings illuminate two fundamental features of the mechanisms of hematin crystallization: the elongated shapes of natural and synthetic hematin crystals and the stabilization of charged groups of hematin and antimalarials by encasing in water clusters. In addition, these findings suggest that hematin crystallization may be controlled by additives that disrupt or reinforce solvent structuring.

Protecting future antimalarials from the trap of resistance: Lessons from artemisinin-based combination therapy (ACT) failures

Having faced increased clinical treatment failures with dihydroartemisinin-piperaquine (DHA-PPQ), Cambodia swapped the first line artemisinin-based combination therapy (ACT) from DHA-PPQ to artesunate-mefloquine given that parasites resistant to piperaquine are susceptible to mefloquine. However, triple mutants have now emerged, suggesting that drug rotations may not be adequate to keep resistance at bay. There is, therefore, an urgent need for alternative treatment strategies to tackle resistance and prevent its spread. A proper understanding of all contributors to artemisinin resistance may help us identify novel strategies to keep artemisinins effective until new drugs become available for their replacement. This review highlights the role of the key players in artemisinin resistance, the current strategies to deal with it and suggests ways of protecting future antimalarial drugs from bowing to resistance as their predecessors did.

Deep learning identifies synergistic drug combinations for treating COVID-19

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

Machine learning methods, databases and tools for drug combination prediction

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

In vitro reduction of Plasmodium falciparum gametocytes: Artemisia spp. tea infusions vs. artemisinin

ETHNOPHARMACOLOGICAL RELEVANCE

Artemisia annua has a long history of use in Southeast Asia where it was used to treat "fever", and A. afra has a similar history in southern Africa. Since their discovery, A. annua use, in particular, has expanded globally with millions of people using the plant in therapeutic tea infusions, mainly to treat malaria.

AIM OF THE STUDY

In this study, we used in vitro studies to query if and how A. annua and A. afra tea infusions being used across the globe affect asexual Plasmodium falciparum parasites, and their sexual gametocytes.

MATERIALS AND METHODS

P. falciparumstrain NF54 was grown in vitro, synchronized, and induced to form gametocytes using N-acetylglucosamine. Cultures during asexual, early, and late stage gametocytogenesis were treated with artemisinin, methylene blue, and A. annua and A. afra tea infusions (5 g DW/L) using cultivars that contained 0-283 μM artemisinin. Asexual parasitemia and gametocytemia were analyzed microscopically. Gametocyte morphology also was scored. Markers of early (PfGEXP5) and late stage (Pfs25) gametocyte gene expression also were measured using RT-qPCR.

RESULTS

Both A. annua and A. afra tea infusions reduced gametocytemia in vitro, and the effect was mainly artemisinin dependent. Expression levels of both marker genes were reduced and also occurred with the effect mainly attributed to artemisinin content of four tested Artemisia cultivars. Tea infusions of both species also inhibited asexual parasitemia and although mainly artemisinin dependent, there was a weak antiparasitic effect from artemisinin-deficient A. afra.

CONCLUSIONS

These results showed that A. annua and to a lesser extent, A. afra, inhibited parasitemia and gametocytemia in vitro.

Dissection of haplotype-specific drug response phenotypes in multiclonal malaria isolates

Natural infections of Plasmodium falciparum, the parasite responsible for the deadliest form of human malaria, often comprise multiple parasite lineages (haplotypes). Multiclonal parasite isolates may exhibit variable phenotypes including different drug susceptibility profiles over time due to the presence of multiple haplotypes. To test this hypothesis, three P. falciparum Cambodian isolates IPC_3445 (MRA-1236), IPC_5202 (MRA-1240) and IPC_6403 (MRA-1285) suspected to be multiclonal were cloned by limiting dilution, and the resulting clones genotyped at 24 highly polymorphic single nucleotide polymorphisms (SNPs). Isolates harbored up to three constituent haplotypes, and exhibited significant variability (p < 0.05) in susceptibility to chloroquine, mefloquine, artemisinin and piperaquine as measured by half maximal drug inhibitory concentration (IC₅₀) assays and parasite survival assays, which measure viability following exposure to pharmacologically relevant concentrations of antimalarial drugs. The IC₅₀ of the most abundant haplotype frequently reflected that of the uncloned parental isolate, suggesting that a single haplotype dominates the antimalarial susceptibility profile and masks the effect of minor frequency haplotypes. These results indicate that phenotypic variability in parasite isolates is often due to the presence of multiple haplotypes. Depending on intended end-use, clinical isolates should be cloned to yield single parasite lineages with well-defined phenotypes and genotypes. The availability of such standardized clonal parasite lineages through NIAID's BEI Resources program will aid research directed towards the development of diagnostics and interventions including drugs against malaria.

Artemisinin-Based Drugs Target the Plasmodium falciparum Heme Detoxification Pathway

Artemisinin (ART)-based antimalarial drugs are believed to exert lethal effects on malarial parasites by alkylating a variety of intracellular molecular targets. Recent work with live parasites has shown that one of the alkylated targets is free heme within the parasite digestive vacuole, which is liberated upon hemoglobin catabolism by the intraerythrocytic parasite, and that reduced levels of heme alkylation occur in artemisinin-resistant parasites. One implication of heme alkylation is that these drugs may inhibit parasite detoxification of free heme via inhibition of heme-to-hemozoin crystallization; however, previous reports that have investigated this hypothesis present conflicting data. By controlling reducing conditions and, hence, the availability of ferrous versus ferric forms of free heme, we modify a previously reported hemozoin inhibition assay to quantify the ability of ART-based drugs to target the heme detoxification pathway under reduced versus oxidizing conditions. Contrary to some previous reports, we find that artemisinins are potent inhibitors of hemozoin crystallization, with effective half-maximal concentrations approximately an order of magnitude lower than those for most quinoline-based antimalarial drugs. We also examine hemozoin and in vitro parasite growth inhibition for drug pairs found in the most commonly used ART-based combination therapies (ACTs). All ACTs examined inhibit hemozoin crystallization in an additive fashion, and all but one inhibit parasite growth in an additive fashion.

Plasmodium Kinases as Potential Drug Targets for Malaria: Challenges and Opportunities

Protein and phosphoinositide kinases have been successfully exploited as drug targets in various disease areas, principally in oncology. In malaria, several protein kinases are under investigation as potential drug targets, and an inhibitor of Plasmodium phosphatidylinositol 4-kinase type III beta (PI4KIIIβ) is currently in phase 2 clinical studies. In this Perspective, we review the potential of kinases as drug targets for the treatment of malaria. Kinases are known to be readily druggable, and many are essential for parasite survival. A key challenge in the design of Plasmodium kinase inhibitors is obtaining selectivity over the corresponding human orthologue(s) and other human kinases due to the highly conserved nature of the shared ATP binding site. Notwithstanding this, there are some notable differences between the Plasmodium and human kinome that may be exploitable. There is also the potential for designed polypharmacology, where several Plasmodium kinases are inhibited by the same drug. Prior to starting the drug discovery process, it is important to carefully assess potential kinase targets to ensure that the inhibition of the desired kinase will kill the parasites in the required life-cycle stages with a sufficiently fast rate of kill. Here, we highlight key target attributes and experimental approaches to consider and summarize the progress that has been made targeting Plasmodium PI4KIIIβ, cGMP-dependent protein kinase, and cyclin-dependent-like kinase 3.

Dynamic Imaging of LDH Inhibition in Tumors Reveals Rapid In Vivo Metabolic Rewiring and Vulnerability to Combination Therapy

The reliance of many cancers on aerobic glycolysis has stimulated efforts to develop lactate dehydrogenase (LDH) inhibitors. However, despite significant efforts, LDH inhibitors (LDHi) with sufficient specificity and in vivo activity to determine whether LDH is a feasible drug target are lacking. We describe an LDHi with potent, on-target, in vivo activity. Using hyperpolarized magnetic resonance spectroscopic imaging (HP-MRSI), we demonstrate in vivo LDH inhibition in two glycolytic cancer models, MIA PaCa-2 and HT29, and we correlate depth and duration of LDH inhibition with direct anti-tumor activity. HP-MRSI also reveals a metabolic rewiring that occurs in vivo within 30 min of LDH inhibition, wherein pyruvate in a tumor is redirected toward mitochondrial metabolism. Using HP-MRSI, we show that inhibition of mitochondrial complex 1 rapidly redirects tumor pyruvate toward lactate. Inhibition of both mitochondrial complex 1 and LDH suppresses metabolic plasticity, causing metabolic quiescence in vitro and tumor growth inhibition in vivo.

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations to become costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combination prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack interpretability, limiting their clinical applications. To address these challenges, we have developed a knowledge-enabled and self-attention transformer boosted deep learning model, TranSynergy, which improves the performance and interpretability of synergistic drug combination prediction. TranSynergy is designed so that the cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method has been developed to deconvolute genes that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidences. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations have been predicted with high confidence for ovarian cancer which has few treatment options. The code is available at https://github.com/qiaoliuhub/drug_combination.

Matrix Drug Screen Identifies Synergistic Drug Combinations to Augment SMAC Mimetic Activity in Ovarian Cancer

Simple Summary

Recurrent ovarian cancer is difficult to treat due to the development of chemotherapy resistance. This resistance develops through multiple mechanisms to include the avoidance of cell death by cancer cells. Prior studies have shown birinapant, a second mitochondrial activator of caspases (SMAC) mimetic drug, to be promising in overcoming this acquired resistance. Despite good tolerability, however, therapy with single-agent birinapant exhibited minimal anti-cancer activity in women with recurrent ovarian cancer. By using a high-throughput drug screen we were able to identify potential therapeutic agents that augment birinapant activity, with docetaxel emerging favorably due to its marked synergy and known utility in the recurrent ovarian cancer setting. We showed that this synergy is the result of several complementary molecular pathways and hope to highlight the promising potential of this therapeutic drug combination for clinical testing where treatment options are often limited.

Abstract

Inhibitor of apoptosis (IAP) proteins are frequently upregulated in ovarian cancer, resulting in the evasion of apoptosis and enhanced cellular survival. Birinapant, a synthetic second mitochondrial activator of caspases (SMAC) mimetic, suppresses the functions of IAP proteins in order to enhance apoptotic pathways and facilitate tumor death. Despite on-target activity, however, pre-clinical trials of single-agent birinapant have exhibited minimal activity in the recurrent ovarian cancer setting. To augment the therapeutic potential of birinapant, we utilized a high-throughput screening matrix to identify synergistic drug combinations. Of those combinations identified, birinapant plus docetaxel was selected for further evaluation, given its remarkable synergy both in vitro and in vivo. We showed that this synergy results from multiple convergent pathways to include increased caspase activation, docetaxel-mediated TNF-α upregulation, alternative NF-kB signaling, and birinapant-induced microtubule stabilization. These findings provide a rationale for the integration of birinapant and docetaxel in a phase 2 clinical trial for recurrent ovarian cancer where treatment options are often limited and minimally effective.

Modulation of Triple Artemisinin-Based Combination Therapy Pharmacodynamics by Plasmodium falciparum Genotype

The first-line treatments for uncomplicated Plasmodium falciparum malaria are artemisinin-based combination therapies (ACTs), consisting of an artemisinin derivative combined with a longer acting partner drug. However, the spread of P. falciparum with decreased susceptibility to artemisinin and partner drugs presents a significant challenge to malaria control efforts. To stem the spread of drug resistant parasites, novel chemotherapeutic strategies are being evaluated, including the implementation of triple artemisinin-based combination therapies (TACTs). Currently, there is limited knowledge on the pharmacodynamic and pharmacogenetic interactions of proposed TACT drug combinations. To evaluate these interactions, we established an in vitro high-throughput process for measuring the drug concentration-response to three distinct antimalarial drugs present in a TACT. Sixteen different TACT combinations were screened against 15 parasite lines from Cambodia, with a focus on parasites with differential susceptibilities to piperaquine and artemisinins. Analysis revealed drug-drug interactions unique to specific genetic backgrounds, including antagonism between piperaquine and pyronaridine associated with gene amplification of plasmepsin II/III, two aspartic proteases that localize to the parasite digestive vacuole. From this initial study, we identified parasite genotypes with decreased susceptibility to specific TACTs, as well as potential TACTs that display antagonism in a genotype-dependent manner. Our assay and analysis platform can be further leveraged to inform drug implementation decisions and evaluate next-generation TACTs.

Phosphatidylinositol synthesis, its selective salvage, and inter-regulation of anionic phospholipids in Toxoplasma gondii

Phosphatidylinositol (PtdIns) serves as an integral component of eukaryotic membranes; however, its biosynthesis in apicomplexan parasites remains poorly understood. Here we show that Toxoplasma gondii-a common intracellular pathogen of humans and animals-can import and co-utilize myo-inositol with the endogenous CDP-diacylglycerol to synthesize PtdIns. Equally, the parasite harbors a functional PtdIns synthase (PIS) containing a catalytically-vital CDP-diacylglycerol phosphotransferase motif in the Golgi apparatus. Auxin-induced depletion of PIS abrogated the lytic cycle of T. gondii in human cells due to defects in cell division, gliding motility, invasion, and egress. Isotope labeling of the PIS mutant in conjunction with lipidomics demonstrated de novo synthesis of specific PtdIns species, while revealing the salvage of other lipid species from the host cell. Not least, the mutant showed decline in phosphatidylthreonine, and elevation of selected phosphatidylserine and phosphatidylglycerol species, indicating a rerouting of CDP-diacylglycerol and homeostatic inter-regulation of anionic phospholipids upon knockdown of PIS. In conclusion, strategic allocation of own and host-derived PtdIns species to gratify its metabolic demand features as a notable adaptive trait of T. gondii. Conceivably, the dependence of T. gondii on de novo lipid synthesis and scavenging can be exploited to develop new anti-infectives.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

Overcoming adaptive therapy resistance in AML by targeting immune response pathways

Targeted inhibitors to oncogenic kinases demonstrate encouraging clinical responses early in the treatment course; however, most patients will relapse because of target-dependent mechanisms that mitigate enzyme-inhibitor binding or through target-independent mechanisms, such as alternate activation of survival and proliferation pathways, known as adaptive resistance. Here, we describe mechanisms of adaptive resistance in FMS-like receptor tyrosine kinase (FLT3)-mutant acute myeloid leukemia (AML) by examining integrative in-cell kinase and gene regulatory network responses after oncogenic signaling blockade by FLT3 inhibitors (FLT3i). We identified activation of innate immune stress response pathways after treatment of FLT3-mutant AML cells with FLT3i and showed that innate immune pathway activation via the interleukin-1 receptor-associated kinase 1 and 4 (IRAK1/4) complex contributes to adaptive resistance in FLT3-mutant AML cells. To overcome this adaptive resistance mechanism, we developed a small molecule that simultaneously inhibits FLT3 and IRAK1/4 kinases. The multikinase FLT3-IRAK1/4 inhibitor eliminated adaptively resistant FLT3-mutant AML cells in vitro and in vivo and displayed superior efficacy as compared to current targeted FLT3 therapies. These findings uncover a polypharmacologic strategy for overcoming adaptive resistance to therapy in AML by targeting immune stress response pathways.

Heterologous Expression, Purification, and Functional Analysis of the Plasmodium falciparum Phosphatidylinositol 4-Kinase IIIβ

Recently, we heterologously expressed, purified, and analyzed the function of the sole Plasmodium falciparum phosphatidylinositol 3-kinase (PI3K), found that the enzyme is a "class III" or "Vps34" PI3K, and found that it is irreversibly inhibited by Fe²⁺-mediated covalent, nonspecific interactions with the leading antimalarial drug, dihydroartemisinin [Hassett, M. R., et al. (2017) Biochemistry 56, 4335-4345]. One of several P. falciparum phosphatidylinositol 4-kinases [putative IIIβ isoform (PfPI4KIIIβ)] has generated similar interest as a druggable target; however, no validation of the mechanism of action for putative PfPI4K inhibitors has yet been possible due to the lack of purified PfPI4KIIIβ. We therefore codon optimized the pfpi4kIIIβ gene, successfully expressed the protein in yeast, and purified an N-lobe catalytic domain PfPI4KIIIβ protein. Using an enzyme-linked immunosorbent assay strategy previously perfected for analysis of PfPI3K (PfVps34), we measured the apparent initial rate, K_m,app(ATP), and other enzyme characteristics and found full activity for the construct and that PfPI4KIIIβ activity is most consistent with the class IIIβ designation. Because several novel antimalarial drug candidates with different chemical scaffolds have been proposed to target PfPI4KIIIβ, we titrated enzyme inhibition for these candidates versus purified PfPI4KIIIβ and PfVps34. We also analyzed the activity versus purified PfPI4KIIIβ mutants previously expressed in P. falciparum selected for resistance to these drugs. Interestingly, we found that a putative PfPI4KIIIβ inhibitor currently in advanced trials (MMV390048; MMV '0048) is a potent inhibitor of both PfVps34 and PfPI4KIIIβ. These data are helpful for further preclinical optimization of an exciting new class of P. falciparum PI kinase inhibitor ("PfPIKi") antimalarial drugs.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

Origin and Spread of Evolving Artemisinin-Resistant Plasmodium falciparum Malarial Parasites in Southeast Asia

In this review, we provide an epidemiological history of the emergence and ongoing spread of evolving Plasmodium falciparum artemisinin resistance (ARTR). Southeast Asia has been the focal point for emergence and spread of multiple antimalarial drug resistance phenomena, and is once again for evolving ARTR, also known as the "delayed clearance phenotype" (DCP). The five countries most impacted, Cambodia, Thailand, Myanmar, Laos, and Vietnam, each have complex histories of antimalarial drug use over many decades, which have in part molded the use of various artemisinin combination therapies (ACTs) within each country. We catalog the use of ACTs, evolving loss of ACT efficacy, and the frequency of pfk13 mutations (mutations associated with ARTR) in the Greater Mekong Subregion and map the historical spread of ARTR/DCP parasites. These data should assist improved surveillance and deployment of next-generation ACTs.

A high-throughput screening platform for Polycystic Kidney Disease (PKD) drug repurposing utilizing murine and human ADPKD cells

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common inherited monogenic disorders, characterized by a progressive decline in kidney function due in part to the formation of fluid-filled cysts. While there is one FDA-approved therapy, it is associated with potential adverse effects, and all other clinical interventions are largely supportive. Insights into the cellular pathways underlying ADPKD have revealed striking similarities to cancer. Moreover, several drugs originally developed for cancer have shown to ameliorate cyst formation and disease progression in animal models of ADPKD. These observations prompted us to develop a high-throughput screening platform of cancer drugs in a quest to repurpose them for ADPKD. We screened ~8,000 compounds, including compounds with oncological annotations, as well as FDA-approved drugs, and identified 155 that reduced the viability of Pkd1-null mouse kidney cells with minimal effects on wild-type cells. We found that 109 of these compounds also reduced in vitro cyst growth of Pkd1-null cells cultured in a 3D matrix. Moreover, the result of the cyst assay identified therapeutically relevant compounds, including agents that interfere with tubulin dynamics and reduced cyst growth without affecting cell viability. Because it is known that several ADPKD therapies with promising outcomes in animal models failed to be translated to human disease, our platform also incorporated the evaluation of compounds in a panel of primary ADPKD and normal human kidney (NHK) epithelial cells. Although we observed differences in compound response amongst ADPKD and NHK cell preparation, we identified 18 compounds that preferentially affected the viability of most ADPKD cells with minimal effects on NHK cells. Our study identifies attractive candidates for future efficacy studies in advanced pre-clinical models of ADPKD.

Prediction of drug combination effects with a minimal set of experiments

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

Identification of plicamycin, TG02, panobinostat, lestaurtinib, and GDC-0084 as promising compounds for the treatment of central nervous system infections caused by the free-living amebae Naegleria, Acanthamoeba and Balamuthia

The free-living amebae Naegleria, Acanthamoeba, and Balamuthia cause rare but life-threatening infections. All three parasites can cause meningoencephalitis. Acanthamoeba can also cause chronic keratitis and both Balamuthia and Acanthamoeba can cause skin and systemic infections. There are minimal drug development pipelines for these pathogens despite a lack of available treatment regimens and high fatality rates. To identify anti-amebic drugs, we screened 159 compounds from a high-value repurposed library against trophozoites of the three amebae. Our efforts identified 38 compounds with activity against at least one ameba. Multiple drugs that bind the ATP-binding pocket of mTOR and PI3K are active, highlighting these compounds as important inhibitors of these parasites. Importantly, 24 active compounds have progressed at least to phase II clinical studies and overall 15 compounds were active against all three amebae. Based on central nervous system (CNS) penetration or exceptional potency against one amebic species, we identified sixteen priority compounds for the treatment of meningoencephalitis caused by these pathogens. The top five compounds are (i) plicamycin, active against all three free-living amebae and previously U.S. Food and Drug Administration (FDA) approved, (ii) TG02, active against all three amebae, (iii and iv) FDA-approved panobinostat and FDA orphan drug lestaurtinib, both highly potent against Naegleria, and (v) GDC-0084, a CNS penetrant mTOR inhibitor, active against at least two of the three amebae. These results set the stage for further investigation of these clinically advanced compounds for treatment of infections caused by the free-living amebae, including treatment of the highly fatal meningoencephalitis.

An ImmunoPEGliposome for Targeted Antimalarial Combination Therapy at the Nanoscale

Combination therapies, where two drugs acting through different mechanisms are administered simultaneously, are one of the most efficient approaches currently used to treat malaria infections. However, the different pharmacokinetic profiles often exhibited by the combined drugs tend to decrease treatment efficacy as the compounds are usually eliminated from the circulation at different rates. To circumvent this obstacle, we have engineered an immunoliposomal nanovector encapsulating hydrophilic and lipophilic compounds in its lumen and lipid bilayer, respectively. The antimalarial domiphen bromide has been encapsulated in the liposome membrane with good efficiency, although its high IC₅₀ of ca. 1 µM for living parasites complicates its use as immunoliposomal therapy due to erythrocyte agglutination. The conjugation of antibodies against glycophorin A targeted the nanocarriers to Plasmodium-infected red blood cells and to gametocytes, the sole malaria parasite stage responsible for the transmission from the human to the mosquito vector. The antimalarials pyronaridine and atovaquone, which block the development of gametocytes, have been co-encapsulated in glycophorin A-targeted immunoliposomes. The co-immunoliposomized drugs have activities significantly higher than their free forms when tested in in vitro Plasmodium falciparum cultures: Pyronaridine and atovaquone concentrations that, when encapsulated in immunoliposomes, resulted in a 50% inhibition of parasite growth had no effect on the viability of the pathogen when used as free drugs.

Artemisinin-Based Antimalarial Drug Therapy: Molecular Pharmacology and Evolving Resistance

The molecular pharmacology of artemisinin (ART)-based antimalarial drugs is incompletely understood. Clinically, these drugs are used in combination with longer lasting partner drugs in several different artemisinin combination therapies (ACTs). ACTs are currently the standard of care against Plasmodium falciparum malaria across much of the world. A harbinger of emerging artemisinin resistance (ARTR), known as the delayed clearance phenotype (DCP), has been well documented in South East Asia (SEA) and is beginning to affect the efficacy of some ACTs. Though several genetic mutations have been associated with ARTR/DCP, a molecular mechanism remains elusive. This paper summarizes our current understanding of ART molecular pharmacology and hypotheses for ARTR/DCP.

On-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm

Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify a set of serum-free formulations that support cell expansions similar to the serum-containing conditions commonly used to culture these cells, by experimentally testing less than 1 × 10-5 % of the total search space. We also demonstrate how this iterative search process can provide insights into factor interactions that contribute to supporting cell expansion.

Resistance to Epigenetic-Targeted Therapy Engenders Tumor Cell Vulnerabilities Associated with Enhancer Remodeling

Drug resistance represents a major challenge to achieving durable responses to cancer therapeutics. Resistance mechanisms to epigenetically targeted drugs remain largely unexplored. We used bromodomain and extra-terminal domain (BET) inhibition in neuroblastoma as a prototype to model resistance to chromatin modulatory therapeutics. Genome-scale, pooled lentiviral open reading frame (ORF) and CRISPR knockout rescue screens nominated the phosphatidylinositol 3-kinase (PI3K) pathway as promoting resistance to BET inhibition. Transcriptomic and chromatin profiling of resistant cells revealed that global enhancer remodeling is associated with upregulation of receptor tyrosine kinases (RTKs), activation of PI3K signaling, and vulnerability to RTK/PI3K inhibition. Large-scale combinatorial screening with BET inhibitors identified PI3K inhibitors among the most synergistic upfront combinations. These studies provide a roadmap to elucidate resistance to epigenetic-targeted therapeutics and inform efficacious combination therapies.

A single nucleotide polymorphism in the Plasmodium falciparum atg18 gene associates with artemisinin resistance and confers enhanced parasite survival under nutrient deprivation

Background

Artemisinin-resistant Plasmodium falciparum has been reported throughout the Greater Mekong subregion and threatens to disrupt current malaria control efforts worldwide. Polymorphisms in kelch13 have been associated with clinical and in vitro resistance phenotypes; however, several studies suggest that the genetic determinants of resistance may involve multiple genes. Current proposed mechanisms of resistance conferred by polymorphisms in kelch13 hint at a connection to an autophagy-like pathway in P. falciparum.

Results

A SNP in autophagy-related gene 18 (atg18) was associated with long parasite clearance half-life in patients following artemisinin-based combination therapy. This gene encodes PfAtg18, which is shown to be similar to the mammalian/yeast homologue WIPI/Atg18 in terms of structure, binding abilities, and ability to form puncta in response to stress. To investigate the contribution of this polymorphism, the atg18 gene was edited using CRISPR/Cas9 to introduce a T38I mutation into a k13-edited Dd2 parasite. The presence of this SNP confers a fitness advantage by enabling parasites to grow faster in nutrient-limited settings. The mutant and parent parasites were screened against drug libraries of 6349 unique compounds. While the SNP did not modulate the parasite’s susceptibility to any of the anti-malarial compounds using a 72-h drug pulse, it did alter the parasite’s susceptibility to 227 other compounds.

Conclusions

These results suggest that the atg18 T38I polymorphism may provide additional resistance against artemisinin derivatives, but not partner drugs, even in the absence of kelch13 mutations, and may also be important in parasite survival during nutrient deprivation.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2532-x) contains supplementary material, which is available to authorized users.

Discovery of the actinoplanic acid pathway in Streptomyces rapamycinicus reveals a genetically conserved synergism with rapamycin

Actinobacteria possess a great wealth of pathways for production of bioactive compounds. Following advances in genome mining, dozens of natural product (NP) gene clusters are routinely found in each actinobacterial genome; however, the modus operandi of this large arsenal is poorly understood. During investigations of the secondary metabolome of Streptomyces rapamycinicus, the producer of rapamycin, we observed accumulation of two compounds never before reported from this organism. Structural elucidation revealed actinoplanic acid A and its demethyl analogue. Actinoplanic acids (APLs) are potent inhibitors of Ras farnesyltransferase and therefore represent bioactive compounds of medicinal interest. Supported with the unique structure of these polyketides and using genome mining, we identified a gene cluster responsible for their biosynthesis in S. rapamycinicus Based on experimental evidence and genetic organization of the cluster, we propose a stepwise biosynthesis of APL, the first bacterial example of a pathway incorporating the rare tricarballylic moiety into an NP. Although phylogenetically distant, the pathway shares some of the biosynthetic principles with the mycotoxins fumonisins. Namely, the core polyketide is acylated with the tricarballylate by an atypical nonribosomal peptide synthetase-catalyzed ester formation. Finally, motivated by the conserved colocalization of the rapamycin and APL pathway clusters in S. rapamycinicus and all other rapamycin-producing actinobacteria, we confirmed a strong synergism of these compounds in antifungal assays. Mining for such evolutionarily conserved coharboring of pathways would likely reveal further examples of NP sets, attacking multiple targets on the same foe. These could then serve as a guide for development of new combination therapies.

Combination therapy to checkmate Glioblastoma: clinical challenges and advances

Combination therapy is increasingly becoming the cornerstone of current day antitumor therapy. Glioblastoma multiforme is an aggressive brain tumor with a dismal median survival post diagnosis and a high rate of disease recurrence. The poor prognosis can be attributed to unique treatment limitations, which include the infiltrative nature of tumor cells, failure of anti-glioma drugs to cross the blood-brain barrier, tumor heterogeneity and the highly metastatic and angiogenic nature of the tumor making cells resistant to chemotherapy. Combination therapy approach is being developed against glioblastoma with new innovative combination drug regimens being tested in preclinical and clinical trials. In this review, we discuss the pathophysiology of glioblastoma, diagnostic markers, therapeutic targeting strategies, current treatment limitations, novel combination therapies in the context of current treatment options and the ongoing clinical trials for glioblastoma therapy.

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.

Small Molecules Identified from a Quantitative Drug Combinational Screen Resensitize Cisplatin's Response in Drug-Resistant Ovarian Cancer Cells

Drug resistance to chemotherapy occurs in many ovarian cancer patients resulting in failure of treatment. Exploration of drug resistance mechanisms and identification of new therapeutics that overcome the drug resistance can improve patient prognosis. Following a quantitative combination screen of 6060 approved drugs and bioactive compounds in a cisplatin-resistant A2780-cis ovarian cancer cell line, 38 active compounds with IC₅₀s under 1 μM suppressed the growth of cisplatin-resistant ovarian cancer cells. Among these confirmed compounds, CUDC-101, OSU-03012, oligomycin A, VE-821, or Torin2 in a combination with cisplatin restored cisplatin's apoptotic response in the A2780-cis cells, while SR-3306, GSK-923295, SNX-5422, AT-13387, and PF-05212384 directly suppressed the growth of A2780-cis cells. One of the mechanisms for overcoming cisplatin resistance in these cells is mediated by the inhibition of epidermal growth factor receptor (EGFR), though not all the EGFR inhibitors are equally active. The increased levels of total EGFR and phosphorylated-EGFR (p-EGFR) in the A2780-cis cells were reduced after the combined treatment of cisplatin with EGFR inhibitors. In addition, a knockdown of EGFR mRNA reduced cisplatin resistance in the A2780-cis cells. Therefore, the top active compounds identified in this work can be studied further as potential treatments for cisplatin-resistant ovarian cancer. The quantitative combinational screening approach is a useful method for identifying effective compounds and drug combinations against drug-resistant cancer cells.