Background: Telehealth services have gained popularity in Malaysia, providing convenient consultations during the COVID-19 pandemic. However, there is limited research on their usage, user demographics and prescribed medications. This study aims to fill that gap by investigating telehealth service utilisation in community pharmacies and identifying trends in common diagnoses and medications prescribed. Methods: A retrospective observational study was conducted using a telehealth services database in Malaysian community pharmacies. Consultation records from January 2019 to December 2021 were extracted using a data collection form. The study identified the service usage over time, demographic profiles of users and the most common diagnoses and prescribed medications. Diagnoses were classified using the International Classification of Disease, 10th Revision (ICD-10), and medications were classified using the Anatomical Therapeutic Chemical (ATC) system. Results: The study included 835,826 telehealth service records, with 88.8% being assisted consultations with e-prescriptions and 11.2% direct consultations. The user population consisted of primarily Malaysians (96.9%), with a mean age of 50 ± 21 years. Both telehealth services saw an increase in unique users over the 3-year study period. There was a moderate correlation between active COVID-19 cases and monthly user count. Assisted consultations were more widely used than direct consultations. Conclusion: This study found an increased usage of telehealth services and its potential to remain as a healthcare system feature in community pharmacies. Further investigation into the impact on medication safety, quality and healthcare delivery is warranted.

In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.