A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria

Inhibitory Efficacy of Main Components of Scutellaria baicalensis on the Interaction between Spike Protein of SARS-CoV-2 and Human Angiotensin-Converting Enzyme II

Blocking the interaction between the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme II (hACE2) protein serves as a therapeutic strategy for treating COVID-19. Traditional Chinese medicine (TCM) treatments containing bioactive products could alleviate the symptoms of severe COVID-19. However, the emergence of SARS-CoV-2 variants has complicated the process of developing broad-spectrum drugs. As such, the aim of this study was to explore the efficacy of TCM treatments against SARS-CoV-2 variants through targeting the interaction of the viral spike protein with the hACE2 receptor. Antiviral activity was systematically evaluated using a pseudovirus system. Scutellaria baicalensis (S. baicalensis) was found to be effective against SARS-CoV-2 infection, as it mediated the interaction between the viral spike protein and the hACE2 protein. Moreover, the active molecules of S. baicalensis were identified and analyzed. Baicalein and baicalin, a flavone and a flavone glycoside found in S. baicalensis, respectively, exhibited strong inhibitory activities targeting the viral spike protein and the hACE2 protein, respectively. Under optimized conditions, virus infection was inhibited by 98% via baicalein-treated pseudovirus and baicalin-treated hACE2. In summary, we identified the potential SARS-CoV-2 inhibitors from S. baicalensis that mediate the interaction between the Omicron spike protein and the hACE2 receptor. Future studies on the therapeutic application of baicalein and baicalin against SARS-CoV-2 variants are needed.

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.

A machine learning approach for early identification of patients with severe imported malaria

BACKGROUND

The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting.

METHODS

This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis.

RESULTS

A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria.

CONCLUSION

In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment.

Prediction and identification of synergistic compound combinations against pancreatic cancer cells

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.

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.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Current status of quinoxaline and quinoxaline 1,4-di-N-oxides derivatives as potential antiparasitic agents

Parasitic diseases are a public health problem, especially in developing countries where millions of people are affected every year. Current treatments have several drawbacks: emerging resistance to the existing drugs, lack of efficacy, and toxic side effects. Therefore, new antiparasitic drugs are urgently needed to treat and control diseases that affect human health, such as malaria, Chagas disease, leishmaniasis, amebiasis, giardiasis schistosomiasis, and filariasis, among others. Quinoxaline is a compound containing a benzene ring and a pyrazine ring. The oxidation of both pyrazine ring nitrogens allows the obtention of quinoxaline 1,4-di-N-oxides (QdNOs) derivatives. By modifying the chemical structure of these compounds, it is possible to obtain a wide variety of biological properties. This review investigated the activity of quinoxaline derivatives and QdNOs against different protozoan parasites and helminths. We also cover the structure-activity relationship (SAR) and summarize the main findings related to their mechanisms of action from published works in recent years. However, further studies are needed to determine specific molecular targets. This review aims to highlight the new development of antiparasitic drugs with better pharmacological profiles than current treatments.

Recent advances in drug repurposing using machine learning

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.

Transcriptional drug repositioning and cheminformatics approach for differentiation therapy of leukaemia cells

Differentiation therapy is attracting increasing interest in cancer as it can be more specific than conventional chemotherapy approaches, and it has offered new treatment options for some cancer types, such as treating acute promyelocytic leukaemia (APL) by retinoic acid. However, there is a pressing need to identify additional molecules which act in this way, both in leukaemia and other cancer types. In this work, we hence developed a novel transcriptional drug repositioning approach, based on both bioinformatics and cheminformatics components, that enables selecting such compounds in a more informed manner. We have validated the approach for leukaemia cells, and retrospectively retinoic acid was successfully identified using our method. Prospectively, the anti-parasitic compound fenbendazole was tested in leukaemia cells, and we were able to show that it can induce the differentiation of leukaemia cells to granulocytes in low concentrations of 0.1 μM and within as short a time period as 3 days. This work hence provides a systematic and validated approach for identifying small molecules for differentiation therapy in cancer.

An insight in anti-malarial potential of indole scaffold: A review

Malaria is a major parasitic disease in tropical and sub-tropical regions. Pertaining to the sustaining resistance in malarial parasite against the available drugs, novel treatment options are the need of the hour. In this resolve recently, focus has shifted to finding the natural alternatives that possess anti-plasmodial activity for combatting malaria. Drawing on the text written in ancient scriptures and Ayurveda, natural compounds are now being screened for their therapeutic properties. Indole is one such natural compound, present in all living organisms, it displays a range of therapeutic activities including anticancer, anti-inflammatory, antimalarial etc. In this review, we have discussed various indole scaffold as well as the semi-synthetic drugs containing indole moiety that have been synthesized for malaria treatment.

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

BACKGROUND

Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters.

METHODS

We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers.

RESULTS

The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location.

CONCLUSION

The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.

Raloxifene as Treatment for Various Types of Brain Injuries and Neurodegenerative Diseases: A Good Start

Recent studies have shown that the selective estrogen receptor modulator (SERM) raloxifene had pronounced protective effects against progressing brain damage after traumatic brain injury (TBI) in mice. These studies, indicating beneficial effects of raloxifene for brain health, prompted the study of the history and present state of knowledge of this topic. It appears that, apart from raloxifene, to date, four nonrelated compounds have shown comparable beneficial effects—fucoidan, pifithrin, SMM-189 (5-dihydroxy-phenyl]-phenyl-methanone), and translocator protein (TSPO) ligands. Raloxifene, however, is ahead of the field, as for more than two decades it has been used in medical practice for various chronic ailments in humans. Thus, apart from different types of animal and cell culture studies, it has also been assessed in various human clinical trials, including assaying its effects on mild cognitive impairments. Regarding cell types, raloxifene protects neurons from cell death, prevents glial activation, ameliorates myelin damage, and maintains health of endothelial cells. At whole central nervous system (CNS) levels, raloxifene ameliorated mild cognitive impairments, as seen in clinical trials, and showed beneficial effects in animal models of Parkinson’s disease. Moreover, with stroke and TBI in animal models, raloxifene showed curative effects. Furthermore, raloxifene showed healing effects regarding multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS) in cell culture. The adverse biological signals typical of these conditions relate to neuronal activity, neurotransmitters and their receptors, plasticity, inflammation, oxidative stress, nitric oxide, calcium homeostasis, cell death, behavioral impairments, etc. Raloxifene favorably modulates these signals toward cell health—on the one hand, by modulating gene expression of the relevant proteins, for example by way of its binding to the cell nuclear estrogen receptors ERα and ERβ (genomic effects) and, on the other hand (nongenomic effects) by modulation of mitochondrial activity, reduction of oxidative stress and programmed cell death, maintaining metabolic balance, degradation of Abeta, and modulation of intracellular cholesterol levels. More specifically regarding Alzheimer’s disease, raloxifene may not cure diagnosed Alzheimer’s disease. However, the onset of Alzheimer’s disease may be delayed or arrested by raloxifene’s capability to attenuate mild cognitive impairment. Mild cognitive impairment is a condition that may precede diagnosis of Alzheimer’s disease. In this review, relatively new insights are addressed regarding the notion that Alzheimer’s disease can be caused by bacterial (as well as viral) infections, together with the most recent findings that raloxifene can counteract infections of at least some bacterial and viral strains. Thus, here, an overview of potential treatments of neurodegenerative disease by raloxifene is presented, and attention is paid to subcellular molecular biological pathways that may be involved.

Correlation Between Malaria-Specific Antibody Profiles and Responses to Artemisinin Combination Therapy for Treatment of Uncomplicated Malaria in Western Kenya

BACKGROUND

The impact of preexisting immunity on the efficacy of artemisinin combination therapy must be examined to monitor resistance, and for implementation of new treatment strategies.

METHODS

Serum samples obtained from a clinical trial in Western Kenya randomized to receive artemether-lumefantrine (AL) or artesunate-mefloquine (ASMQ) were screened for total immunoglobulin G against preerythrocytic and erythrocytic antigens. The association and correlation between different variables, and impact of preexisting immunity on parasite slope half-life (t½) was determined.

RESULTS

There was no significant difference in t½, but the number of individuals with lag phase was significantly higher in the AL than in the ASMQ arm (29 vs 13, respectively; P < .01). Circumsporozoite protein-specific antibodies correlate positively with t½ (AL, P = .03; ASMQ, P = .09), but negatively with clearance rate in both study arms (AL, P = .16; ASMQ, P = .02). The t½ correlated negatively with age in ASMQ group. When stratified based on t½, the antibody titers against circumsporozoite protein and merozoite surface protein 1 were significantly higher in participants who cleared parasites rapidly in the AL group (P = .01 and P = .02, respectively).

CONCLUSION

Data presented here define immunoprofiles associated with distinct responses to 2 different antimalarial drugs, revealing impact of preexisting immunity on the efficacy of artemisinin combination therapy regimens in a malaria-holoendemic area.

CLINICAL TRIALS REGISTRATION

NCT01976780.

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.