Eventos adversos no sistema nervoso central potencialmente relacionados aos medicamentos utilizados na COVID-19: revisão de escopo

Objetivo.

Identificar eventos adversos no sistema nervoso central (SNC) potencialmente associados ao uso de medicamentos para profilaxia ou tratamento da COVID-19, bem como caracterizar os indivíduos acometidos.

Métodos.

Desenvolveu-se uma revisão de escopo a partir de estratégia de busca nas bases de dados PubMed, EMBASE, SciELO, Scopus, Cummulative Index to Nursing and Allied Health Literature (CINAHL) e Biblioteca Virtual em Saúde (BVS). Foram incluídos estudos com indivíduos que utilizaram medicamentos como medida profilática ou curativa para COVID-19 e que apresentaram ao menos um evento adverso no SNC. Foram excluídos os artigos que reportaram eventos adversos no SNC potencialmente associados a medicamentos para outras condições de saúde.

Resultados.

Recuperaram-se 1 547 artigos, dos quais oito atenderam aos critérios de elegibilidade. Em sete estudos, o desenho foi do tipo observacional. Foram analisados 3 035 indivíduos, dos quais 1 701 eram profissionais da saúde e 1 978, mulheres. A terapia mais utilizada foi a curativa (n = 5), com hidroxicloroquina, cloroquina, lopinavir/ritonavir e azitromicina. Os eventos adversos comumente descritos foram dor de cabeça, tontura, distúrbios de humor e sonolência. Suicídio foi o evento grave mais frequente. Seis eventos foram inesperados para hidroxicloroquina, cloroquina e doxiciclina (mioclonias, tremor, distúrbio de marcha, disgeusia, hiperidrose e inquietação).

Conclusão.

Os eventos adversos no SNC foram inespecíficos e, geralmente, potencialmente associados ao uso de hidroxicloroquina em monoterapia ou em associação para tratamento curativo da COVID-19. Os dados corroboram a relação desfavorável de risco/benefício desses medicamentos na prevenção e no manejo dos sinais e sintomas da infecção por SARS-CoV-2.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Ugi four-component polymerization of amino acid derivatives: a combinatorial tool for the design of polypeptoids

The combinatorial Ugi-4C polymerization of amino acid derivatives is attractive for the future development of polypeptoids and resulting applications.

Discovering Anti-Cancer Drugs via Computational Methods

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Analysis of new drugs registered in Brazil in view of the Unified Health System and the disease burden

The most important aspect of a new drug in terms of public health is its therapeutic value and benefit it provides for the patient and for the society. The aim of this study was to analyze new drugs registered in Brazil between 2003 and 2013 with respect to Pharmaceutical Assistance programs within the Brazilian health system and to the disease burden in the country. In our retrospective cohort study, new drugs registered in Brazil were identified through document analysis of databases and publicly available documents from National Health Surveillance Agency. The data on disease burden in Brazil was obtained from the Global Burden of Disease Study 2012, published by the World Health Organization. The level of therapeutic innovation was determined using the Motola algorithm. Although a total of 159 new medicines were used in the cohort, only 28 (17.6%) were classified as important therapeutic innovations. There is a disproportionate relationship between the percentage of new drugs and the burden of disease, with an under-representation of drugs for infectious respiratory diseases, heart disease, and digestive diseases. Incentive strategies for research and development of medicines should be prioritized to reduce the disparity regarding the burden of disease and to help develop innovative medicines necessary to improve health throughout the country.

Terminology of bioanalytical methods (IUPAC Recommendations 2018)

Recommendations are given concerning the terminology of methods of bioanalytical chemistry. With respect to dynamic development particularly in the analysis and investigation of biomacromolecules, terms related to bioanalytical samples, enzymatic methods, immunoanalytical methods, methods used in genomics and nucleic acid analysis, proteomics, metabolomics, glycomics, lipidomics, and biomolecules interaction studies are introduced.

"New drug" designations for new therapeutic entities: new active substance, new chemical entity, new biological entity, new molecular entity

This Perspective addresses ambiguities in designations of "new drugs" intended as new therapeutic entities (NTEs). Designation of an NTE as a new drug is significant, as it may confer regulatory exclusivity, an important incentive for development of novel compounds. Such designations differ between jurisdictions according to their drug laws and drug regulations. Chemical, biological, and innovative drugs are addressed in turn. The terms new chemical entity (NCE), new molecular entity (NME), new active substance (NAS), and new biological entity (NBE) as applied in worldwide jurisdictions are clarified. Differences between them are explored through case studies showing why new drugs have different periods of exclusivity in different jurisdictions or none at all. Finally, this Perspective recommends that in future, for the purpose of new drug compilations, NME is used for a new chemical drug, NBE for a new biological drug, and the combined designation NTE should refer to either an NME or an NBE.