Thai Bulletin of Pharmaceutical Sciences

FROM ORIGINATOR PREMIUM TO PRICE CONVERGENCE: THE GENERIC EFFECT IN THAILAND’S PROCUREMENT

Kusawadee Maluangnon — 2026-06-05

This study examined the pricing behavior of originator medicines in Thailand’s public procurement system following two key market events: the first generic entry (FGE) and the expansion of market competition to three or more active competitors. A cross-sectional quantitative analysis was conducted using procurement data from the Thai Comptroller General’s Department covering fiscal years 2018–2022 and encompassing 923 public hospitals nationwide. Products with generic registrations during 2019–2021 were identified, and median annual procurement unit prices were calculated before and after the occurrence of FGE and the first attainment of markets with at least three competing manufacturers or importers. From 75,755 procurement records covering 81 GPUs and 588 TPUs, 23 GPUs met the analytic criteria (11 with no generic purchases over five years; 12 with generic competition). Among the 13 GPUs experiencing first generic entry (FGE), immediate first-year originator price reductions were observed in only 2 cases (15%; bevacizumab, olopatadine). When the first generic was supplied by the same manufacturer as the originator (authorized generic), no clear price decrease was detected. For biologics, announcing a Maximum Procurement Price (MPP) at the time of FGE was associated with an average ~30% reduction in originator prices within the first year. In markets with ≥3 competitors, procurement prices trended downward consistently in line with reference-price implementation, and in some instances originator products withdrew from market. Overall, the findings indicate that FGE by itself does not reliably reduce prices; a MPP and more competitors are essential for achieving meaningful price reductions.

PREDICTION OF NEUTROPENIA IN CANCER PATIENTS RECEIVING CHEMOTHERAPY USING MACHINE LEARNING TECHNIQUE

Nutcha Sutthisanseth — 2026-04-27

Neutropenia is a common complication in patients with cancer receiving chemotherapy. Without appropriate prevention or management, this condition can lead to severe complications and may be life-threatening. Previous studies revealed that machine learning techniques can be effectively predicted chemotherapy-related complications. The objective of this study was to develop and compare the optimal machine learning models for predicting neutropenia in cancer patients receiving chemotherapy. A retrospective dataset of 511 patients was collected from the hospital information system of Ratchaphiphat Hospital between January 1, 2022, and March 31, 2024. Data were derived from medical records, physical examination, and laboratory information, and were further screened to identify relevant variables prior to model development. The dataset was split into two groups; a training set (80%) and a testing set (20%). The training dataset was balanced using the SMOTE-NC method. Five machine learning techniques: Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN) were developed using optimal hyperparameters. Model selection was based on accuracy, precision, recall, and F1-measure. Model performance was evaluated using the testing dataset, based on its sensitivity, specificity, and AUC. The results demonstrated that the best-performing models were those developed using RF and AdaBoost, with sensitivity, specificity, and AUC values of 100%, 98.21%, and 1.000, respectively. Therefore, RF and AdaBoost, which are ensemble learning techniques, were the most suitable models for predicting neutropenia.

IMPACT OF OBESITY ON CANCER: FROM PATHOGENESIS TO PHARMACOTHERAPEUTIC CHALLENGES AND MANAGEMENT OF TREATMENT-INDUCED WEIGHT GAIN

Nutthada Areepium — 2026-04-27

Obesity is a critical risk factor influencing both cancer incidence and prognosis. This narrative review aims to provide readers with a comprehensive understanding of the obesity–cancer relationship across three key dimensions: (1) the pathogenic mechanisms by which obesity promotes tumor development and progression, (2) the impact of obesity on anticancer drug pharmacokinetics and the associated challenges in dose optimization, and (3) proactive pharmaceutical care strategies for obese cancer patients, including the management of treatment-induced weight gain. Pathogenetically, obesity promotes tumor progression through three principal mechanisms: chronic low-grade inflammation, hyperinsulinemia-driven dysregulation of the Insulin/IGF-1 signaling axis leading to aberrant cancer cell proliferation, and alterations in sex hormone metabolism that favor the growth of hormone-sensitive cancers. Regarding pharmacotherapy, obesity significantly alters the pharmacokinetics of anticancer agents, particularly by increasing the volume of distribution for lipophilic drugs and impairing drug clearance, thereby heightening the risk of severe adverse reactions including cardiotoxicity and peripheral neuropathy. Clinical evidence supports dosing based on actual body weight to prevent subtherapeutic exposure. Furthermore, this review highlights sarcopenic obesity, frequently exacerbated by corticosteroids and hormonal therapies, as a clinically underrecognized concern. Pharmacists play a pivotal role in proactive pharmaceutical care by optimizing dose individualization, monitoring drug safety, and managing metabolic complications to enhance therapeutic efficacy and patient quality of life.

EFFICACY AND SAFETY OF POLYMYXIN B IN CRITICALLY ILL PATIENTS WITH DRUG-RESISTANT GRAM-NEGATIVE BACTERIAL INFECTIONS

Chayada Changchamni — 2026-05-18

Polymyxin B has been reintroduced as an important therapeutic option for multidrug-resistant
Gram-negative bacterial infections which the global incidence continues to rise. Polymyxin B is administered in its active form, allowing plasma concentrations to reach pharmacokinetic targets more rapidly. Antibacterial efficacy is associated with a steady-state area under the concentration-time curve over 24 hours (AUCss,24h) of 50–100 mg·h/L, representing the therapeutic window that balances efficacy and nephrotoxicity. For intravenous administration, a loading dose of 2.0–2.5 mg/kg is recommended, followed by 1.25–1.50 mg/kg every 12 hours. Optimal dosing remains controversial, particularly regarding adjustments based on renal function, body weight, and renal replacement therapy. Furthermore, for infections in the lungs or central nervous system, adjunctive local administration including inhalation and intrathecal or intraventricular injection might be considered in case of suboptimal response to intravenous antibiotics. Compared with colistin, the efficacy of polymyxin B for drug-resistant Gram-negative infections in most studies is generally comparable, but polymyxin B is associated with a lower rate of nephrotoxicity. Neurotoxicity and skin hyperpigmentation are reported more frequently than with colistin. Therefore, the use of polymyxin B in critically ill patients requires individualized dosing decisions based on the minimum inhibitory concentration of the causative pathogen, the site of infection, disease severity, and organ function, with close monitoring of renal function and adverse effects throughout the course of treatment.