Expert Opinion on Drug Discovery最新文献

Introduction: Selenium possesses numerous advantageous properties in the field of medicine, and a variety of selenium-containing compounds have been documented to exhibit anti-HIV activity. This paper aims to categorize these compounds and conduct SAR analysis to offer guidance for drug design and optimization.

Areas covered: The authors present a comprehensive review of the reported SAR analysis conducted on selenium-based compounds against HIV, accompanied by a concise discussion regarding the pivotal role of selenium in drug development.

Expert opinion: In addition to the conventional bioisosterism strategy, advanced strategies such as covalent inhibition, fragment-based growth and drug repositioning can also be incorporated into research on selenium-containing anti-HIV drugs. Ebselen, which acts as an HIV capsid inhibitor, serves as a valuable probe compound for the discovery of novel HIV integrase inhibitors. Furthermore, it is crucial not to underestimate the potential toxicity associated with organic selenium compounds despite no reported instances of severe toxicity.

Introduction: Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively.

Areas covered: The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD.

Expert opinion: As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.

Introduction: Analyses of orally administered FDA-approved drugs from 1990 to 1993 enabled the identification of a set of physiochemical properties known as Lipinski's Rule of Five (Ro5). The original Ro5 and extended versions still remain the reference criteria for drug development programs. Since many bioactive compounds do not conform to the Ro5, we validated the relevance of and adherence to these rulesets in a contemporary cohort of FDA-approved drugs.

Areas covered: The authors noted that a significant proportion of FDA-approved orally administered parent compounds from 2011 to 2022 deviate from the original Ro5 criteria (~38%) or the Ro5 with extensions (~53%). They then evaluated if a contemporary Ro5 criteria (cRo5) could be devised to better predict oral bioavailability. Furthermore, they discuss many case studies showcasing the need for and benefit of increasing the size of certain compounds and cover several evolving strategies for improving oral bioavailability.

Expert opinion: Despite many revisions to the Ro5, the authors find that no single proposed physiochemical rule has universal concordance with absolute oral bioavailability. Innovations in drug delivery and formulation have dramatically expanded the range of physicochemical properties and the chemical diversity for oral administration.

Introduction: Psychiatry is one of the medical disciplines that suffers most from a lack of innovation in its therapeutic arsenal. Many failures in drug candidate trials can be explained by pharmacological properties that have been poorly assessed upstream, in terms of brain passage, brain target binding and clinical outcomes. Positron emission tomography can provide pharmacokinetic and pharmacodynamic data to help select candidate-molecules for further clinical trials.

Areas covered: This review aims to explain and discuss the various methods using positron-emitting radiolabeled molecules to trace the cerebral distribution of the drug-candidate or indirectly measure binding to its therapeutic target. More than an exhaustive review of PET studies in psychopharmacology, this article highlights the contributions this technology can make in drug discovery applied to psychiatry.

Expert opinion: PET neuroimaging is the only technological approach that can, in vivo in humans, measure cerebral delivery of a drug candidate, percentage and duration of target binding, and even the pharmacological effects. PET studies in a small number of subjects in the early stages of the development of a psychotropic drug can therefore provide the pharmacokinetic/pharmacodynamic data required for subsequent clinical evaluation. While PET technology is demanding in terms of radiochemical, radiopharmacological and nuclear medicine expertise, its integration into the development process of new drugs for psychiatry has great added value.

Introduction: Animal models play a crucial role in breast cancer research, in particular mice and rats, who develop mammary tumors that closely resemble their human counterparts. These models allow the study of mechanisms behind breast carcinogenesis, as well as the efficacy and safety of new, and potentially more effective and advantageous therapeutic approaches. Understanding the advantages and disadvantages of each model is crucial to select the most appropriate one for the research purpose.

Area covered: This review provides a concise overview of the animal models available for breast cancer research, discussing the advantages and disadvantages of each one for searching new and more effective approaches to treatments for this type of cancer.

Expert opinion: Rodent models provide valuable information on the genetic alterations of the disease, the tumor microenvironment, and allow the evaluation of the efficacy of chemotherapeutic agents. However, in vivo models have limitations, and one of them is the fact that they do not fully mimic human diseases. Choosing the most suitable model for the study purpose is crucial for the development of new therapeutic agents that provide better care for breast cancer patients.

Introduction: The drug discovery and development 'valley of death' remains a challenge for promising new therapies originating from academic research laboratories. Drug discovery support centers and accelerators have been established to provide monetary and scientific support, but limited available funding along with cultural and expertise gaps remain obstacles for many promising technologies.

Areas covered: In this meta-opinion article, the authors summarize the literature around obstacles that academic drug discovery projects face, along with potential solutions and best practices. Topics covered include funding challenges, regulatory education, reproducibility, along with cultural and organizational considerations. It describes one accelerator in particular-Critical Path Institute's Translational Therapeutics Accelerator (TRxA)-that aims to overcome several of the mentioned challenges.

Expert opinion: The 'valley of death' remains a stubborn but not insurmountable part of the academic drug discovery and development landscape. Purposely designed accelerators can help, complementing more traditional intra- and extramural funding support.

Introduction: Malaria remains a devastating infectious disease with hundreds of thousands of casualties each year. Antimalarial drug resistance has been a threat to malaria control and elimination for many decades and is still of concern today. Despite the continued effectiveness of current first-line treatments, namely artemisinin-based combination therapies, the emergence of drug-resistant parasites in Southeast Asia and even more alarmingly the occurrence of resistance mutations in Africa is of great concern and requires immediate attention.

Areas covered: A comprehensive overview of the mechanisms underlying the acquisition of drug resistance in Plasmodium falciparum is given. Understanding these processes provides valuable insights that can be harnessed for the development and selection of novel antimalarials with reduced resistance potential. Additionally, strategies to mitigate resistance to antimalarial compounds on the short term by using approved drugs are discussed.

Expert opinion: While employing strategies that utilize already approved drugs may offer a prompt and cost-effective approach to counter antimalarial drug resistance, it is crucial to recognize that only continuous efforts into the development of novel antimalarial drugs can ensure the successful treatment of malaria in the future. Incorporating resistance propensity assessment during this developmental process will increase the likelihood of effective and enduring malaria treatments.

Introduction: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.

Areas covered: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.

Expert opinion: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

Introduction: Network representation can give a holistic view of relationships for biomedical entities through network topology. Link prediction estimates the probability of link formation between the pair of unconnected nodes. In the drug discovery process, the link prediction method not only enables the detection of connectivity patterns but also predicts the effects of one biomedical entity to multiple entities simultaneously and vice versa, which is useful for many applications.

Areas covered: The authors provide a comprehensive overview of network link prediction in drug discovery. Link prediction methodologies such as similarity-based approaches, embedding-based approaches, probabilistic model-based approaches, and preprocessing methods are summarized with examples. In addition to describing their properties and limitations, the authors discuss the applications of link prediction in drug discovery based on the relationship between biomedical concepts.

Expert opinion: Link prediction is a powerful method to infer the existence of novel relationships in drug discovery. However, link prediction has been hampered by the sparsity of data and the lack of negative links in biomedical networks. With preprocessing to balance positive and negative samples and the collection of more data, the authors believe it is possible to develop more reliable link prediction methods that can become invaluable tools for successful drug discovery.