Quantification of orelabrutinib in human plasma and cerebrospinal fluid by liquid chromatography tandem mass spectrometry

Recent advances in the biosynthesis and industrial biotechnology of Gamma-amino butyric acid

GABA (Gamma-aminobutyric acid), a crucial neurotransmitter in the central nervous system, has gained significant attention in recent years due to its extensive benefits for human health. The review focused on recent advances in the biosynthesis and production of GABA. To begin with, the investigation evaluates GABA-producing strains and metabolic pathways, focusing on microbial sources such as Lactic Acid Bacteria, Escherichia coli, and Corynebacterium glutamicum. The metabolic pathways of GABA are elaborated upon, including the GABA shunt and critical enzymes involved in its synthesis. Next, strategies to enhance microbial GABA production are discussed, including optimization of fermentation factors, different fermentation methods such as co-culture strategy and two-step fermentation, and modification of the GABA metabolic pathway. The review also explores methods for determining glutamate (Glu) and GABA levels, emphasizing the importance of accurate quantification. Furthermore, a comprehensive market analysis and prospects are provided, highlighting current trends, potential applications, and challenges in the GABA industry. Overall, this review serves as a valuable resource for researchers and industrialists working on GABA advancements, focusing on its efficient synthesis processes and various applications, and providing novel ideas and approaches to improve GABA yield and quality.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

The preclinical discovery and development of orelabrutinib as a novel treatment option for B-cell lymphoid malignancies

INTRODUCTION

Bruton's tyrosine kinase (BTK) inhibitors have recently been approved for clinical use against several B-cell indolent lymphoid malignancies, both as single agents or in combination. One second-generation BTK inhibitor that is being developed for the treatment of B-cell hematological malignancies, as well as for autoimmune disorders, is orelabrutinib.

AREAS COVERED

This paper reviews recent developments in the use of orelabrutinib against B-cell indolent lymphoid malignancies such as chronic lymphocytic leukemia, mantle cell lymphoma, diffuse large B-cell lymphoma, Waldenstrom macroglobulinemia and central nervous system lymphoma. Google Scholar and PubMed were initially searched for articles, and the corpus of articles was broadened by reviewing the references of the identified papers. All were in English. The corpus comprised papers from 2016 to April 2023. In addition, a manual search was performed of conference proceedings from the last five years of The American Society of Hematology, American Society of Clinical Oncology and the European Hematology Association.

EXPERT OPINION

Orelabrutinib is an active drug in indolent and aggressive B-cell lymphoid malignancies. It demonstrates high selectivity, good efficacy and an excellent safety profile. Nevertheless, further clinical trials are required to optimize its use. In addition, several other highly selective BTK inhibitors are being examined in early-phase studies.