Comparing in vivo, in vitro and in silico methods and integrated strategies for chemical assessment: problems and prospects

Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv .Scientific contributionWe propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.

Curcumin as a natural potential drug candidate against important zoonotic viruses and prions: A narrative review

Zoonotic diseases are major public health concerns and undeniable threats to human health. Among Zoonotic diseases, zoonotic viruses and prions are much more difficult to eradicate, as they result in higher infections and mortality rates. Several investigations have shown curcumin, the active ingredient of turmeric, to have wide spectrum properties such as anti-microbial, anti-vascular, anti-inflammatory, anti-tumor, anti-neoplastic, anti-oxidant, and immune system modulator properties. In the present study, we performed a comprehensive review of existing in silico, in vitro, and in vivo evidence on the antiviral (54 important zoonotic viruses) and anti-prion properties of curcumin and curcuminoids in PubMed, Google Scholar, Science Direct, Scopus, and Web of Science databases. Database searches yielded 13,380 results, out of which 216 studies were eligible according to inclusion criteria. Of 216 studies, 135 (62.5%), 24 (11.1%), and 19 (8.8%) were conducted on the effect of curcumin and curcuminoids against SARS-CoV-2, Influenza A virus, and dengue virus, respectively. This review suggests curcumin and curcuminoids as promising therapeutic agents against a wide range of viral zoonoses by targeting different proteins and signaling pathways.

Reinforcement learning strategies in cancer chemotherapy treatments: A review

BACKGROUND AND OBJECTIVE

Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning.

METHODS

Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning.

RESULTS

The insights established in the chemotherapy-planning problem associated with the Reinforcement Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques.

CONCLUSIONS

This study reviewed the recent researches relevant to the topic, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.

Potential of Vitamin B6 Dioxime Analogues to Act as Cholinesterase Ligands

Seven pyridoxal dioxime quaternary salts (1-7) were synthesized with the aim of studying their interactions with human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The synthesis was achieved by the quaternization of pyridoxal monooxime with substituted 2-bromoacetophenone oximes (phenacyl bromide oximes). All compounds, prepared in good yields (43-76%) and characterized by 1D and 2D NMR spectroscopy, were evaluated as reversible inhibitors of cholinesterase and/or reactivators of enzymes inhibited by toxic organophosphorus compounds. Their potency was compared with that of their monooxime analogues and medically approved oxime HI-6. The obtained pyridoxal dioximes were relatively weak inhibitors for both enzymes (Ki = 100-400 µM). The second oxime group in the structure did not improve the binding compared to the monooxime analogues. The same was observed for reactivation of VX-, tabun-, and paraoxon-inhibited AChE and BChE, where no significant efficiency burst was noted. In silico analysis and molecular docking studies connected the kinetic data to the structural features of the tested compound, showing that the low binding affinity and reactivation efficacy may be a consequence of a bulk structure hindering important reactive groups. The tested dioximes were non-toxic to human neuroblastoma cells (SH-SY5Y) and human embryonal kidney cells (HEK293).

Investigating SARS-CoV-2 Susceptibility in Animal Species: A Scoping Review

In the early stages of response to the SARS-CoV-2 pandemic, it was imperative for researchers to rapidly determine what animal species may be susceptible to the virus, under low knowledge and high uncertainty conditions. In this scoping review, the animal species being evaluated for SARS-CoV-2 susceptibility, the methods used to evaluate susceptibility, and comparing the evaluations between different studies were conducted. Using the PRISMA-ScR methodology, publications and reports from peer-reviewed and gray literature sources were collected from databases, Google Scholar, the World Organization for Animal Health (OIE), snowballing, and recommendations from experts. Inclusion and relevance criteria were applied, and information was subsequently extracted, categorized, summarized, and analyzed. Ninety seven sources (publications and reports) were identified which investigated 649 animal species from eight different classes: Mammalia, Aves, Actinopterygii, Reptilia, Amphibia, Insecta, Chondrichthyes, and Coelacanthimorpha. Sources used four different methods to evaluate susceptibility, in silico, in vitro, in vivo, and epidemiological analysis. Along with the different methods, how each source described "susceptibility" and evaluated the susceptibility of different animal species to SARS-CoV-2 varied, with conflicting susceptibility evaluations evident between different sources. Early in the pandemic, in silico methods were used the most to predict animal species susceptibility to SARS-CoV-2 and helped guide more costly and intensive studies using in vivo or epidemiological analyses. However, the limitations of all methods must be recognized, and evaluations made by in silico and in vitro should be re-evaluated when more information becomes available, such as demonstrated susceptibility through in vivo and epidemiological analysis.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Natural products and their derivatives against coronavirus: A review of the non-clinical and pre-clinical data

Several corona viral infections have created serious threats in the last couple of decades claiming the death of thousands of human beings. Recently, corona viral epidemic raised the issue of developing effective antiviral agents at the earliest to prevent further losses. Natural products have always played a crucial role in drug development process against various diseases, which resulted in screening of such agents to combat emergent mutants of corona virus. This review focuses on those natural compounds that showed promising results against corona viruses. Although inhibition of viral replication is often considered as a general mechanism for antiviral activity of most of the natural products, studies have shown that some natural products can interact with key viral proteins that are associated with virulence. In this context, some of the natural products have antiviral activity in the nanomolar concentration (e.g., lycorine, homoharringtonine, silvestrol, ouabain, tylophorine, and 7-methoxycryptopleurine) and could be leads for further drug development on their own or as a template for drug design. In addition, a good number of natural products with anti-corona virus activity are the major constituents of some common dietary supplements, which can be exploited to improve the immunity of the general population in certain epidemics.

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

Background:

Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows.

Objective:

The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds.

Materials and Methods:

For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance.

Results:

Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds.

Conclusion:

It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

QSAR Methods

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

A Permeability-Limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic acid (PFOA) in Male Rats

Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic substances, such as perfluorooctanoic acid (PFOA), in organisms. However, most existing PBPK models have been based on the flow-limited assumption and largely rely on in vivo data for parametrization. In this study, we propose a permeability-limited PBPK model to estimate the toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular uptake and efflux of PFOA via both passive diffusion and transport facilitated by various membrane transporters, association with serum albumin in circulatory and extracellular spaces, and association with intracellular proteins in liver and kidney. Model performance is assessed using seven experimental data sets extracted from three different studies. Comparing model predictions with these experimental data, our model successfully predicts the toxicokinetics and tissue distribution of PFOA in rats following exposure via both IV and oral routes. More importantly, rather than requiring in vivo data fitting, all PFOA-related parameters were obtained from in vitro assays. Our model thus provides an effective framework to test in vitro-in vivo extrapolation and holds great promise for predicting toxicokinetics of per- and polyfluorinated alkyl substances in humans.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

Integrated testing strategies for safety assessments

Despite the fact that toxicology uses many stand-alone tests, a systematic combination of several information sources very often is required: Examples include: when not all possible outcomes of interest (e.g., modes of action), classes of test substances (applicability domains), or severity classes of effect are covered in a single test; when the positive test result is rare (low prevalence leading to excessive false-positive results); when the gold standard test is too costly or uses too many animals, creating a need for prioritization by screening. Similarly, tests are combined when the human predictivity of a single test is not satisfactory or when existing data and evidence from various tests will be integrated. Increasingly, kinetic information also will be integrated to make an in vivo extrapolation from in vitro data. Integrated Testing Strategies (ITS) offer the solution to these problems. ITS have been discussed for more than a decade, and some attempts have been made in test guidance for regulations. Despite their obvious potential for revamping regulatory toxicology, however, we still have little guidance on the composition, validation, and adaptation of ITS for different purposes. Similarly, Weight of Evidence and Evidence-based Toxicology approaches require different pieces of evidence and test data to be weighed and combined. ITS also represent the logical way of combining pathway-based tests, as suggested in Toxicology for the 21st Century. This paper describes the state of the art of ITS and makes suggestions as to the definition, systematic combination, and quality assurance of ITS.

Toxicity of nanomaterials

Nanoscience has matured significantly during the last decade as it has transitioned from bench top science to applied technology. Presently, nanomaterials are used in a wide variety of commercial products such as electronic components, sports equipment, sun creams and biomedical applications. There are few studies of the long-term consequences of nanoparticles on human health, but governmental agencies, including the United States National Institute for Occupational Safety and Health and Japan's Ministry of Health, have recently raised the question of whether seemingly innocuous materials such as carbon-based nanotubes should be treated with the same caution afforded known carcinogens such as asbestos. Since nanomaterials are increasing a part of everyday consumer products, manufacturing processes, and medical products, it is imperative that both workers and end-users be protected from inhalation of potentially toxic NPs. It also suggests that NPs may need to be sequestered into products so that the NPs are not released into the atmosphere during the product's life or during recycling. Further, non-inhalation routes of NP absorption, including dermal and medical injectables, must be studied in order to understand possible toxic effects. Fewer studies to date have addressed whether the body can eventually eliminate nanomaterials to prevent particle build-up in tissues or organs. This critical review discusses the biophysicochemical properties of various nanomaterials with emphasis on currently available toxicology data and methodologies for evaluating nanoparticle toxicity (286 references).

Integrated Testing Strategies and the Prediction of Toxic Hazard

Over the past 20-30 years there has been a move towards reducing the use of animals in toxicity testing for industrial chemicals, pharmaceuticals, personal care and household products, for economic, scientific and animal welfare reasons. The need for alternatives has been emphasised by the EU REACH regulation, which requires the evaluation of tens of thousands of new and existing chemicals, and also within the pharmaceutical industry owing to the increasing rate at which drugs are being withdrawn from the market due to adverse effects not detected during preclinical testing. Significant effort is being placed into the development of non-animal test procedures using existing data, bioinformatics, in chemico, in silico and in vitro approaches and ethical human studies. Information from these diverse sources needs to be used intelligently and selectively leading to the development of what have become known as Integrated Testing Strategies (ITS). In this chapter factors that need to be considered in the development, evaluation, acceptance and use of ITS will be discussed.