Irinotecan-Induced Toxicity: A Pharmacogenetic Study Beyond UGT1A1

Unraveling the pathogenesis of myelosuppression and therapeutic potential of natural products

BACKGROUND

Myelosuppression is a serious and common complication of radiotherapy and chemotherapy in cancer patients and is characterized by a reduction of peripheral blood cells. This condition not only compromises the efficacy of treatment but also increases the risk of patient death. Natural products are emerging as promising adjuvant therapies due to their antioxidant properties, ability to modulate immune responses, and capacity to stimulate haematopoietic stem cell proliferation. These therapies demonstrate significant potential in ameliorating myelosuppression.

METHODS

A systematic review of the literature was performed utilizing the search terms "natural products," "traditional Chinese medicine," and "myelosuppression" across prominent databases, including Google Scholar, PubMed, and Web of Science. All pertinent literature was meticulously analysed and summarized. The objective of this study was to perform a pertinent analysis to elucidate the mechanisms underlying myelosuppression and to categorize and synthesize information on natural products and traditional Chinese medicines employed for the therapeutic management of myelosuppression.

RESULTS

Myelosuppression resulting from drug and radiation exposure, viral infections, and exosomes is characterized by multiple underlying mechanisms involving immune factors, target genes, and the activation of diverse signalling pathways, including the (TGF-β)/Smad pathway. Recently, traditional Chinese medicine monomers and compounds, including more than twenty natural products, such as Astragalus and Angelica, have shown promising potential as therapeutics for ameliorating myelosuppression. These natural products exert their effects by modulating haematopoietic stem cells, immune factors, and critical signalling pathways.

CONCLUSIONS

Understanding the various mechanisms of myelosuppression facilitates the exploration of natural product therapies and biological target identification for evaluating herbal medicine efficacy. This study aimed to establish a foundation for the clinical application of natural products and provide methodologies and technical support for exploring additional treatments for myelosuppression.

Regulation of carboxylesterases and its impact on pharmacokinetics and pharmacodynamics: an up-to-date review

INTRODUCTION

Carboxylesterase 1 (CES1) and carboxylesterase 2 (CES2) are among the most abundant hydrolases in humans, catalyzing the metabolism of numerous clinically important medications, such as methylphenidate and clopidogrel. The large interindividual variability in the expression and activity of CES1 and CES2 affects the pharmacokinetics (PK) and pharmacodynamics (PD) of substrate drugs.

AREAS COVERED

This review provides an up-to-date overview of CES expression and activity regulations and examines their impact on the PK and PD of CES substrate drugs. The literature search was conducted on PubMed from inception to January 2024.

EXPERT OPINION

Current research revealed modest associations of CES genetic polymorphisms with drug exposure and response. Beyond genomic polymorphisms, transcriptional and posttranslational regulations can also significantly affect CES expression and activity and consequently alter PK and PD. Recent advances in plasma biomarkers of drug-metabolizing enzymes encourage the research of plasma protein and metabolite biomarkers for CES1 and CES2, which could lead to the establishment of precision pharmacotherapy regimens for drugs metabolized by CESs. Moreover, our understanding of tissue-specific expression and substrate selectivity of CES1 and CES2 has shed light on improving the design of CES1- and CES2-activated prodrugs.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.