In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids

Chemoselective Synthesis and Anti-Kinetoplastidal Properties of 2,6-Diaryl-4H-tetrahydro-thiopyran-4-one S-Oxides: Their Interplay in a Cascade of Redox Reactions from Diarylideneacetones

2,6-Diaryl-4H-tetrahydro-thiopyran-4-ones and corresponding sulfoxide and sulfone derivatives were designed to lower the major toxicity of their parent anti-kinetoplatidal diarylideneacetones through a prodrug effect. Novel diastereoselective methodologies were developed and generalized from diarylideneacetones and 2,6-diaryl-4H-tetrahydro-thiopyran-4-ones to allow the introduction of a wide substitution profile and to prepare the related S-oxides. The in vitro biological activity and selectivity of diarylideneacetones, 2,6-diaryl-4H-tetrahydro-thiopyran-4-ones, and their S-sulfoxide and sulfone metabolites were evaluated against Trypanosoma brucei brucei, Trypanosoma cruzi, and various Leishmania species in comparison with their cytotoxicity against human fibroblasts hMRC-5. The data revealed that the sulfides, sulfoxides, and sulfones, in which the Michael acceptor sites are temporarily masked, are less toxic against mammal cells while the anti-trypanosomal potency was maintained against T. b. brucei, T. cruzi, L. infantum, and L. donovani, thus confirming the validity of the prodrug strategy. The mechanism of action is proposed to be due to the involvement of diarylideneacetones in cascades of redox reactions involving the trypanothione system. After Michael addition of the dithiol to the double bonds, resulting in an elongated polymer, the latter-upon S-oxidation, followed by syn-eliminations-fragments, under continuous release of reactive oxygen species and sulfenic/sulfonic species, causing the death of the trypanosomal parasites in the micromolar or submicromolar range with high selectivity indexes.

Preliminary insight on diarylpentanoids as potential antimalarials: In silico, in vitro pLDH and in vivo zebrafish toxicity assessment

Malaria remains a major public health problem worldwide, including in Southeast Asia. Chemotherapeutic agents such as chloroquine (CQ) are effective, but problems with drug resistance and toxicity have necessitated a continuous search for new effective antimalarial agents. Here we report on a virtual screening of ∼300 diarylpentanoids and derivatives, in search of potential Plasmodium falciparum lactate dehydrogenase (PfLDH) inhibitors with acceptable drug-like properties. Several molecules with binding affinities comparable to CQ were chosen for in vitro validation of antimalarial efficacy. Among them, MS33A, MS33C and MS34C are the most promising against CQ-sensitive (3D7) with EC50 values of 1.6, 2.5 and 3.1 μM, respectively. Meanwhile, MS87 (EC50 of 1.85 μM) shown the most active against the CQ-resistant Gombak A strain, and MS33A and MS33C the most effective P. knowlesi inhibitors (EC50 of 3.6 and 5.1 μM, respectively). The in vitro cytotoxicity of selected diarylpentanoids (MS33A, MS33C, MS34C and MS87) was tested on Vero mammalian cells to evaluate parasite selectivity (SI), showing moderate to low cytotoxicity (CC50 > 82 μM). In addition, MS87 exhibited a high SI and the lowest resistance index (RI), suggesting that MS87 may exert effective parasite inhibition with low resistance potential in the CQ-resistant P. falciparum strain. Furthermore, the in vivo toxicity of the molecules on early embryonic development, the cardiovascular system, heart rate, motor activity and apoptosis were assessed in a zebrafish animal model. The overall results indicate the preliminary potential of diarylpentanoids, which need further investigation for their development as new antimalarial agents.

Diarylpentanoids, the privileged scaffolds in antimalarial and anti-infectives drug discovery: A review

Asia is a hotspot for infectious diseases, including malaria, dengue fever, tuberculosis, and the pandemic COVID-19. Emerging infectious diseases have taken a heavy toll on public health and the economy and have been recognized as a major cause of morbidity and mortality, particularly in Southeast Asia. Infectious disease control is a major challenge, but many surveillance systems and control strategies have been developed and implemented. These include vector control, combination therapies, vaccine development, and the development of new anti-infectives. Numerous newly discovered agents with pharmacological anti-infective potential are being actively and extensively studied for their bioactivity, toxicity, selectivity, and mode of action, but many molecules lose their efficacy over time due to resistance developments. These facts justify the great importance of the search for new, effective, and safe anti-infectives. Diarylpentanoids, a curcumin derivative, have been developed as an alternative with better bioavailability and metabolism as a therapeutic agent. In this review, the mechanisms of action and potential targets of antimalarial drugs as well as the classes of antimalarial drugs are presented. The bioactivity of diarylpentanoids as a potential scaffold for a new class of anti-infectives and their structure-activity relationships are also discussed in detail.

Bioactive Clerodane Diterpenoids from the Leaves of Casearia coriacea Vent

Casearia coriacea Vent., an endemic plant from the Mascarene Islands, was investigated following its antiplasmodial potentialities highlighted during a previous screening. Three clerodane diterpene compounds were isolated and identified as being responsible for the antiplasmodial activity of the leaves of the plant: caseamembrin T (1), corybulosin I (2), and isocaseamembrin E (3), which exhibited half maximal inhibitory concentrations (IC₅₀) of 0.25 to 0.51 µg/mL. These compounds were tested on two other parasites, Leishmania mexicana mexicana and Trypanosoma brucei brucei, to identify possible selectivity in one of them. Although these products possess both antileishmanial and antitrypanosomal properties, they displayed selectivity for the malaria parasite, with a selectivity index between 6 and 12 regarding antitrypanosomal activity and between 25 and 100 regarding antileishmanial activity. These compounds were tested on three cell lines, breast cancer cells MDA-MB-231, pulmonary adenocarcinoma cells A549, and pancreatic carcinoma cells PANC-1, to evaluate their selectivity towards Plasmodium. This has not enabled us to establish selectivity for Plasmodium, but has revealed the promising activity of compounds 1-3 (IC₅₀ < 2 µg/mL), particularly against pancreatic carcinoma cells (IC₅₀ < 1 µg/mL). The toxicity of the main compound, caseamembrin T (1), was then evaluated on zebrafish embryos to extend our cytotoxicity study to normal, non-cancerous cells. This highlighted the non-negligible toxicity of caseamembrin T (1).

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum

Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC₅₀ values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

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.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Duration of Storage Reduced Erythrocytes Profiles and Plasmodium Viability in Donor Blood

BACKGROUND

Malaria screening for blood derived from any donors prior to transfusions is a standard procedure that should be performed; but, in fact, it is not routinely conducted. In case of the blood is infected with Plasmodium spp., the survival of parasites may be depending on, or even influencing, the profile of red blood cells (RBCs).

METHODS

This observational longitudinal study was conducted upon 55 bags of donor blood that randomly selected. Malaria infections were detected using Rapid Diagnostic Test/RDT with thin and thick blood smear confirmation. The changes of Plasmodium spp. viability and RBCs profiles, as well as other hematological parameters, were observed from the results of routine hematological examinations which were performed on days 1,7,14 and 21 of storage.

RESULTS

Among 55 blood samples, there were 17 and 38 bags, respectively, positive and negative for malaria, then used for analysis as the case and control groups. There were significant decreasing values (p<0.05) of all routine blood examination parameters of donor blood, started from days 1, 7, 14, 21, and 28. There were no differences in decreasing profiles between those infected and non-infected donor blood (p>0.05). On days 21 and 28 none of the positive samples still contained parasites.

CONCLUSION

Erythrocytes profiles of donor blood significantly decreased with the duration of storage, but were not influenced by the presence of Plasmodium spp.

A Chemographic Audit of anti-Coronavirus Structure-activity Information from Public Databases (ChEMBL)

Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of "anti-CoV" map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800 M organic compounds.

Classification models for predicting the antimalarial activity against Plasmodium falciparum

Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on- andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease-modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the β-amyloid precursor protein (βAPP) and β-amyloid (Aβ) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

Multi-Anti-Parasitic Activity of Arylidene Ketones and Thiazolidene Hydrazines against Trypanosoma cruzi and Leishmania spp

A series of fifty arylideneketones and thiazolidenehydrazines was evaluated against Leishmania infantum and Leishmania braziliensis. Furthermore, new simplified thiazolidenehydrazine derivatives were evaluated against Trypanosoma cruzi. The cytotoxicity of the active compounds on non-infected fibroblasts or macrophages was established in vitro to evaluate the selectivity of their anti-parasitic effects. Seven thiazolidenehydrazine derivatives and ten arylideneketones had good activity against the three parasites. The IC₅₀ values for T. cruzi and Leishmania spp. ranged from 90 nM-25 µM. Eight compounds had multi-trypanocidal activity against T. cruzi and Leishmania spp. (the etiological agents of cutaneous and visceral forms). The selectivity of these active compounds was better than the three reference drugs: benznidazole, glucantime and miltefosine. They also had low toxicity when tested in vivo on zebrafish. Trying to understand the mechanism of action of these compounds, two possible molecular targets were investigated: triosephosphate isomerase and cruzipain. We also used a molecular stripping approach to elucidate the minimal structural requirements for their anti-T. cruzi activity.

QSAR modeling and chemical space analysis of antimalarial compounds

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.