Dendrimers as drug delivery systems for oncotherapy: Current status of promising applications.

Cancer affects millions of people worldwide, causing death and serious health problems. Despite significant investment in the development of new anticancer compounds, there are still several limitations that can still be found. Many compounds exhibit high levels of toxicity and low bioavailability. Therefore, it is urgent to design safer, more effective, and particularly more selective compounds for oncological treatment. Dendrimers are polymeric structures that have been shown to be potential drug nanocarriers to overcome physicochemical, pharmacokinetic, and indirect pharmacodynamic issues. Due to their versatility, they can be used in the design of nanovaccines, lipophilic complexes, amphiphilic complexes, smart nanocomplexes, and others. This work targets the use of dendrimers in oncological treatment and their importance and effectiveness as drug delivery systems for the development of new therapies. For this review, only publications from the last two years are considered in this review.

2D/3D-QSAR Model Development Based on a Quinoline Pharmacophoric Core for the Inhibition of Plasmodium falciparum: An In Silico Approach with Experimental Validation.

Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.

Synthesis of Chiral Acyclic Pyrimidine Nucleoside Analogues from DHAP-Dependent Aldolases.

Dihydroxyacetone phosphate (DHAP)-dependent aldolases catalyze the aldol addition of DHAP to a variety of aldehydes and generate compounds with two stereocenters. This reaction is useful to synthesize chiral acyclic nucleosides, which constitute a well-known class of antiviral drugs currently used. In such compounds, the chirality of the aliphatic chain, which mimics the open pentose residue, is crucial for activity. In this work, three DHAP-dependent aldolases: fructose-1,6-biphosphate aldolase from rabbit muscle, rhanmulose-1-phosphate aldolase from Thermotoga maritima, and fuculose-1-phosphate aldolase from Escherichia coli, were used as biocatalysts. Aldehyde derivatives of thymine and cytosine were used as acceptor substrates, generating new acyclic nucleoside analogues containing two new stereocenters with conversion yields between 70% and 90%. Moreover, structural analyses by molecular docking were carried out to gain insights into the diasteromeric excess observed.

Structure-guided drug design targeting Abl kinase: how structure and regulation can assist in designing new drugs.

The human protein Abelson kinase (Abl), a tyrosine kinase, plays a pivotal role in developing chronic myeloid leukemia (CML). Abl's involvement in various signaling pathways underscores its significance in regulating fundamental biological processes, including DNA damage responses, actin polymerization, and chromatin structural changes. The discovery of the Bcr-Abl oncoprotein, resulting from a chromosomal translocation in CML patients, revolutionized the understanding and treatment of the disease. The introduction of targeted therapies, starting with interferon-alpha and culminating in the development of tyrosine kinase inhibitors (TKIs) like imatinib, significantly improved patient outcomes. However, challenges such as drug resistance and side effects persist, indicating the necessity of research into novel therapeutic strategies. This review describes advancements in Abl kinase inhibitor development, emphasizing rational compound design from structural and regulatory information. Strategies, including bivalent inhibitors, PROTACs, and compounds targeting regulatory domains, promise to overcome resistance and minimize side effects. Additionally, leveraging the intricate structure and interactions of Bcr-Abl may provide insights into developing inhibitors for other kinases. Overall, this review highlights the importance of continued research into Abl kinase inhibition and its broader implications for therapeutic interventions targeting kinase-driven diseases. It provides valuable insights and strategies that may guide the development of next-generation therapies.

Structure-Metabolism Relationships of Benzimidazole Derivatives with anti-Trypanosoma cruzi Activity for Chagas Disease.

This study introduces further insights from the hit-to-lead optimization process involving a series of benzimidazole derivatives acting as inhibitors of the cruzain enzyme, which targets Trypanosoma cruzi, the causative parasite of Chagas disease. Here, we present the design, synthesis and biological evaluation of 30 new compounds as a third generation of benzimidazole analogues with trypanocidal activity, aiming to enhance our understanding of their pharmacokinetic profiles and establish a structure-metabolism relationships within the series. The design of these new analogues was guided by the analysis of previous pharmacokinetic results, considering identified metabolic sites and biotransformation studies. This optimization resulted in the discovery of two compounds (42 e and 49 b) exhibiting enhanced metabolic stability, anti-Trypanosoma cruzi activity compared to benznidazole (the reference drug for Chagas disease), as well as being non-cruzain inhibitors, and demonstrating a satisfactory in vitro pharmacokinetic profile. These findings unveil a new subclass of aminobenzimidazole and rigid compounds, which offer potential for further exploration in the quest for discovering novel classes of antichagasic compounds.

A critical assessment of bioactive compounds databases.

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

[Box: see text].

The impact of biomolecule interactions on the cytotoxic effects of rhenium(I) tricarbonyl complexes.

Rhenium complexes show great promise as anticancer drug candidates. Specifically, compounds with a Re(CO)3(NN)(py)+ core in their architecture have shown cytotoxicity equal to or greater than that of well-established anticancer drugs based on platinum or organic molecules. This study aimed to evaluate how the strength of the interaction between rhenium(I) tricarbonyl complexes fac-[Re(CO)3(NN)(py)]+, NN = 1,10-phenanthroline (phen), dipyrido[3,2-f:2',3'-h]quinoxaline (dpq) or dipyrido[3,2-a:2'3'-c]phenazine (dppz) and biomolecules (protein, lipid and DNA) impacted the corresponding cytotoxic effect in cells. Results showed that fac-[Re(CO)3(dppz)(py)]+ has higher Log Po/w and binding constant (Kb) with biomolecules (protein, lipid and DNA) compared to complexes of fac-[Re(CO)3(phen)(py)]+ and fac-[Re(CO)3(dpq)(py)]+. As consequence, fac-[Re(CO)3(dppz)(py)]+ exhibited the highest cytotoxicity (IC50 = 8.5 µM for HeLa cells) for fac-[Re(CO)3(dppz)(py)]+ among the studied compounds (IC50 > 15 µM). This highest cytotoxicity of fac-[Re(CO)3(dppz)(py)]+ are probably related to its lipophilicity, higher permeation of the lipid bilayers of cells, and a more potent interaction of the dppz ligand with biomolecules (protein and DNA). Our findings open novel avenues for rational drug design and highlight the importance of considering the chemical structures of rhenium complexes that strongly interact with biomolecules (proteins, lipids, and DNA).

The Search for Antidotes Against Ricin.

The castor plant (Ricinus communis) is primarily known for its seeds, which contain a unique fatty acid called ricinoleic acid with several industrial and commercial applications. Castor seeds also contain ricin, a toxin considered a chemical and biological warfare agent. Despite years of investigation, there is still no effective antidote or vaccine available. However, some progress has been made, and the development of an effective treatment may be on the horizon. To provide an updated overview of this issue, we have conducted a comprehensive review of the literature on the current state of research in the fight against ricin. This review is based on the reported research and aims to address the challenges faced by researchers, as well as highlight the most successful cases achieved thus far. Our goal is to encourage the scientific community to continue their efforts in this critical search.

In silico drug design strategies for discovering novel tuberculosis therapeutics.

INTRODUCTION: Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments. AREAS COVERED: In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis. EXPERT OPINION: These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations.

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea.

New fraternine analogues: Evaluation of the antiparkinsonian effect in the model of Parkinson's disease.

Venom-derived peptides are important sources for the development of new therapeutic molecules, especially due to their broad pharmacological activity. Previously, our research group identified a novel natural peptide, named fraternine, with promising effects for the treatment of Parkinson's disease. In the present paper, we synthesized three peptides bioinspired in fraternine: fra-10, fra-14, and fra-24. They were tested in the 6-OHDA-induced model of parkinsonism, quantifying motor coordination, levels of TH+ neurons in the substantia nigra pars compacta (SN), and inflammation mediators TNF-α, IL-6, and IL-1ß in the cortex. Peptides fra-14 and fra-10 improved the motor coordination in relation to 6-OHDA lesioned animals. However, most of the peptides were toxic in the doses applied. All three peptides reduced the intensity of the lesion induced rotations in the apomorphine test. Fra-24 higher dose increased the number of TH+ neurons in SN and reduced the concentration of TNF-α in the cortex of 6-OHDA lesioned mice. Overall, only the peptide fra-24 presented a neuroprotection effect on dopaminergic neurons of SN and a reduction of cytokine TNF-α levels, making it worthy of consideration for the treatment of PD.

In silico identification of a biarylamine acting as agonist at human ß3 adrenoceptors and exerting BRL37344-like effects on mouse metabolism.

Human ß3-adrenoceptor (ß3AR) agonists were considered potential agents for the treatment of metabolic disorders. However, compounds tested as ß3AR ligands have shown marked differences in pharmacological profile in rodent and human species, although these compounds remain attractive as they were successfully repurposed for the therapy of urinary incontinence. In this work, some biarylamine compounds were designed and tested in silico as potential ß3AR agonists on 3-D models of mouse or human ß3ARs. Based on the theoretical results, we identified, synthesized and tested a biarylamine compound (polibegron). In CHO-K1 cells expressing the human ß3AR, polibegron and the ß3AR agonist BRL 37344 were partial agonists for stimulating cAMP accumulation (50 and 57% of the response to isoproterenol, respectively). The potency of polibegron was 1.71- and 4.5-fold higher than that of isoproterenol and BRL37344, respectively. These results indicate that polibegron acts as a potent, but partial, agonist at human ß3ARs. In C57BL/6N mice with obesity induced by a high-fat diet, similar effects of the equimolar intraperitoneal administration of polibegron and BRL37344 were observed on weight, visceral fat and plasma levels of glucose, cholesterol and triglycerides. Similarities and differences between species related to ligand-receptor interactions can be useful for drug designing.

Estudo estrutural e comparativo entre Sirtuínas 2 de diferentes espécies de Leishmania / Structural and comparative study between Sirtuins 2 of different species of Leishmania by means of Comparative Modeling and Molecular Dynamic

As leishmanioses são doenças negligenciadas que afetam mais de um bilhão e meio de pessoas ao redor do mundo, principalmente nos países em desenvolvimento, provocando grandes impactos socioeconômicos. Os fármacos disponíveis para o tratamento dessas doenças são ineficazes e apresentam graves efeitos adversos. O processo de pesquisa de novos fármacos envolve, entre outras coisas, a seleção de alvos bioquímicos essenciais para a sobrevivência e desenvolvimento do agente causador. Neste sentido, a Sirtuína 2, uma enzima epigenética com atividade hidrolase essencial para a sobrevivência dos parasitas do gênero Leishmania se apresenta como um alvo validado na busca de novos fármacos contra essas parasitoses. O planejamento de fármacos baseado na estrutura do receptor requer o conhecimento da estrutura tridimensional da proteína alvo. Desta forma, a elucidação estrutural e um estudo minucioso das Sirtuínas das várias espécies do gênero Leishmania apresenta-se como uma importante abordagem na aplicação desta estratégia na busca por agentes quimioterápicos. Até o momento, na família Trypanosomatidae, a única estrutura tridimensional resolvida experimentalmente de uma enzima Sirtuína 2 é a da espécie L. infantum. Assim, este trabalho aplicou a abordagem de Modelagem Comparativa utilizando o software Modeller na construção de modelos da Sir2rp1 das espécies L. infantum, L. major e L. braziliensis, cujas sequências de aminoácidos foram extraídas do banco de dados UNIProt. Os modelos construídos foram validados por meio da função de escore DOPE do Modeller e dos servidores PROCHECK, MolProbity e QMEAN, avaliando sua qualidade estereoquímica e seu enovelamento. Os ligantes naturais da enzima foram sobrepostos nos modelos construídos por alinhamento estrutural utilizando o software PyMol e os complexos validados foram submetidos a simulações de Dinâmica Molecular através do pacote GROMACS. Os complexos refinados foram então analisados por meio dos softwares PyMol e LigPlotPlus e dos pacotes GROMACS e gmx_MMPBSA, e foram estudados os sítios de ligação dos substratos e os resíduos de aminoácidos relevantes envolvidos em sua ligação e reconhecimento. A Modelagem Comparativa da Sirtuína 2 humana e seus homólogos das espécies L. infantum, L. major e L. braziliensis, as simulações de Dinâmica Molecular realizadas com os modelos enzimáticos construídos e validados complexados com seus ligantes naturais, os cálculos de energia de interação entre os modelos e seus substratos e o estudo estrutural comparativo realizado entre eles nos fornecem uma base teórica para a busca de novos inibidores da Sirtuína 2 que sejam mais seletivos e potentes contra as enzimas parasitárias, abrindo caminho para o desenvolvimento de candidatos a fármacos leishmanicidas mais seguros e eficazes

Leishmaniasis are neglected diseases that affect more than one and a half billion people around the world, mainly in developing countries, causing major socioeconomic impacts. The drugs available for the treatment of these diseases are ineffective and have serious adverse effects. The process of researching new drugs involves, among other things, the selection of biochemical targets essential for the survival and development of the causative agent. In this sense, Sirtuin 2, an epigenetic enzyme with hydrolase activity essential for the survival of parasites of the Leishmania genus, presents itself as a validated target in the search for new drugs against these parasites. Structure-Based Drug Design requires knowledge of the three-dimensional structure of the target protein. In this way, structural elucidation and a detailed study of Sirtuins from various species of the genus Leishmania presents itself as an important approach in the application of this strategy in the search for chemotherapeutic agents. To date, in the Trypanosomatidae family, the only experimentally resolved three-dimensional structure of a Sirtuin 2 enzyme is that of the species L. infantum. Thus, this work applied the Comparative Modeling approach using the Modeller software in the construction of Sir2rp1 models of the species L. infantum, L. major and L. braziliensis, whose amino acid sequences were retrieved from the UNIProt database. The constructed models were validated using Modeller's DOPE score function and the PROCHECK, MolProbity and QMEAN servers, evaluating their stereochemical quality and folding. The enzyme's natural ligands were superimposed on the built models by structural alignment using the PyMol software and the validated complexes were subjected to Molecular Dynamics simulations using the GROMACS package. The refined complexes were then analyzed using the PyMol and LigPlotPlus softwares and the GROMACS and gmx_MMPBSA packages, and the substrate binding sites and relevant amino acid residues involved in their binding and recognition were studied. The Comparative Modeling of human Sirtuin 2 and its homologues from the species L. infantum, L. major and L. braziliensis, the Molecular Dynamics simulations carried out with the constructed and validated enzymatic models complexed with their natural ligands, the interaction energy calculations between the models and their substrates and the comparative structural study carried out between them provide us with a theoretical basis for the search for new Sirtuin 2 inhibitors that are more selective and potent against the parasitic enzymes, paving the way for the development of safer and more effective leishmanicidal drug candidates

Aplicação de ferramentas de quimioinformática na comparação da estrutura tridimensional da dihidrofolato redutase expressa em diferentes organismos / Application of cheminformatics tools for comparing dihydrofolate reductase's tridimensional structure expressed by different organisms

A quimioinformática, definida como o emprego de técnicas informáticas na solução de problemas da química, evolui em conjunto com o desenvolvimento de ferramentas computacionais e é de grande relevância para o planejamento racional de fármacos ao otimizar etapas do desenvolvimento de novas moléculas e economizar recursos e tempo. Dentre as técnicas disponíveis destacam-se o planejamento de fármacos baseado na estrutura e no ligante, que quando combinadas auxiliam na identificação e otimização de moléculas ativas frente a alvos farmacológicos. A Dihidrofolato Redutase (DHFR) é uma importante enzima da via dos folatos que catalisa a redução do dihidrofolato em tetrahidrofolato, utilizando NADPH como cofator, reação essencial para a replicação celular, visto que este ciclo resulta na síntese de precursores das bases nitrogenadas que compõem o DNA, consequentemente, inibidores de DHFR são utilizados no tratamento de infecções bacterianas e alguns tipos de câncer. Trypanosoma cruzi, protozoário causador da doença de chagas, é um dos organismos que expressam a DHFR, além do próprio Homo sapiens. Analisaram-se ligantes conhecidos e as estruturas da proteína expressa pelos dois organismos, visando identificar pontos de divergência que possam ser explorados no planejamento de moléculas seletivas para o tratamento da doença de Chagas. Os 6 modelos cristalográficos de T. cruzi e 2 de H. sapiens foram obtidos do banco de dados de proteínas (PDB) após aplicação de filtros de qualidade. Foram analisadas as sequências de aminoácidos dos modelos, com o uso do Cluster Ômega, sua estrutura tridimensional com os programas Pymol e Chimera X, além da análise das cavidades proteicas com o CavityPlus, que também gerou os farmacóforos de ambos alvos. A análise de estrutura primária identificou mutações em três aminoácidos nos cristais do parasita, que podem ser explicados por diferentes caminhos evolutivos de grupos segregados, embora nenhuma mutação observada esteja em regiões de sítio ativo. A análise dos modelos permitiu que fossem identificados os 25 aminoácidos que estão a menos de 5 Å de distância dos ligantes de T. cruzi, sendo 5 aminoácidos responsáveis por interações de hidrogênio com pelo menos um dos ligantes analisados. Destes, 18 se repetem na proteína humana ou são substituídos por outro aminoácido que mantém a mesma interação. Quanto às diferenças observadas, destacam-se a asparagina 44 substituída por uma prolina na proteína humana e a prolina 92, substituída por uma lisina. A análise de cavidades identificou três cavidades em cada proteína, embora somente as cavidades correspondentes ao sítio ativo sejam druggables. A cavidade da proteína humana é maior e mais alongada, além de apresentar o aspecto de um túnel, enquanto a cavidade da proteína parasita é mais aberta, tal abertura permite que ligantes com o anel benzeno meta substituídos explorem uma região existente na cavidade de T. cruzi que é fechada na humana. O farmacóforo de ambas proteínas foi identificado, apresentando diferenças no tamanho e angulação que também podem ser explorados no planejamento de fármacos seletivos

Chemoinformatic, defined as the use of informatic techniques to solve chemical problems, has evolved together with new computational tools and it is quite important for rational drug designing, by optimizing different steps on the development pipeline of new molecules, saving resources and time. From all the available tools, structure and ligand based drug design shall be highlighted, when combined, they support the identification and optimization of active molecules from pharmaceutical targets. Dihydrofolate reductase (DHFR) is an important enzyme of the folate pathway that catalyzes the reduction of dihydrofolate to tetrahydrofolate, by using NADPH as cofactor. This reaction is essential for cell replication, as this pathway results in the synthesis of nucleobases that build the DNA. That's the reason why DHFR inhibitors are used for treating bacterial infections and some types of cancer. Trypanosoma cruzi, a protozoa that causes Chagas disease, is one of the organisms that express DHFR, besides Homo sapiens itself. This work analyzed known ligands and the structure of the protein expressed by both organisms, aiming to identify divergence points that could be explored for designing selective drugs for Chagas disease treatment. The 6 proteins crystallographic models from T. cruzi and 2 from H. sapiens were obtained from protein data bank (PDB) after the application of quality filters. The amino acid sequence of each model was analyzed by Clustal Omega, its tridimensional structure by Pymol and Chimera X and the cavity analysis by CavityPlus, that also generated the pharmacophore from both targets. The primary structure analysis identified mutations on three amino acids on the parasite christal, which may be explained by different evolutive paths from segregated groups, although none of the observed mutations are on the active site region. The model's analysis allowed the identification of 24 amino acids that are closer than 5 Å from the T. cruzi ligands, 5 of them responsible for hydrogen interactions on at least one of the ligands analyzed. 18 of them are repeated on the human protein or are replaced by another amino acid that preserves the same interaction. As by the differences observed that shall be highlighted, asparagine 44 is replaced by a proline on the human protein, and proline 92 by a lysin. The cavity analysis identified three cavities on each protein, although only the cavities of the active site are druggables. The human protein cavity is bigger and longer, besides it looks like its a tunnel, when the parasite protein is open, that opening allows ligands with benzene ring meta substituted to explore the existing regions of the T. cruzi protein that is closed on the human protein. Lastly, the pharmacophore from both proteins was identified, it shows differences on size and angulation that also could be explored in the designing of selective drugs

Overview of the New Bioactive Heterocycles as Targeting Topoisomerase Inhibitors Useful Against Colon Cancer.

Colorectal cancer (CRC) is the third most common worldwide cancer with high mortality. Factors such as more effective screening programs and improvements in treatment modalities have favored a decrease in the incidence and mortality rate of colorectal cancer in the last three decades. Metastatic CRC is incurable in most cases, and therapy using multiple drugs can increase patients' life expectancy by 2 to 3 years. Chemotherapy is the primary treatment, and fluoropyrimidines correspond to the first treatment line. They can be used in monotherapy or therapeutic schemes of oxaliplatin, FOLFOX (intravenous fluorouracil, leucovorin, and oxaliplatin), and CAPOX (oral capecitabine and oxaliplatin) or regimens based on Irinotecan, such FOLFIRI (fluorouracil, leucovorin, and Irinotecan) and CAPIRI (capecitabine and Irinotecan). Like Camptothecin, irinotecan and other analogs have a mechanism of action based on forming a ternary complex with Topoisomerase I and DNA by reversibly binding, providing DNA damage and consequent cell death. This way, topoisomerases are vital enzymes for DNA maintenance and cell viability. Thus, here we will review the main works demonstrating the correlation between the inhibition of different isoforms of topoisomerases and the in vitro cytotoxic activity in colon cancer. The findings revealed that natural compounds, semi-synthetic and synthetic analogs showed potential cytotoxicity against several colon cancer cell lines in vitro and that this activity was often accompanied by the ability to inhibit type I and II topoisomerases, demonstrating that these enzymes can be promising drug targets for the development of new chemotherapeutics against colon cancer.

Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules.

Virtual small molecule libraries are valuable resources for identifying bioactive compounds in virtual screening campaigns and improving the quality of libraries in terms of physicochemical properties, complexity, and structural diversity. In this context, the computational-aided design of libraries focused against antidiabetic targets can provide novel alternatives for treating type II diabetes mellitus (T2DM). In this work, we integrated the information generated to date on compounds with antidiabetic activity, advances in computational methods, and knowledge of chemical transformations available in the literature to design multi-target compound libraries focused on T2DM. We evaluated the novelty and diversity of the newly generated library by comparing it with antidiabetic compounds approved for clinical use, natural products, and multi-target compounds tested in vivo in experimental antidiabetic models. The designed libraries are freely available and are a valuable starting point for drug design, chemical synthesis, and biological evaluation or further computational filtering. Also, the compendium of 280 transformation rules identified in a medicinal chemistry context is made available in the linear notation SMIRKS for use in other chemical library enumeration or hit optimization approaches.

Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni.

Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 µM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.