A critical study on availability and price variation between different brands: Impact on access to medicines

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features—the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets—that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.

Genotoxicity assessment of two common curing weeds: Hyptis suaveolens (L.) Poir. and Leucas indica (L.) R. Br

Hyptis suaveolens and Leucas indica, two common weeds were selected for the present study, to reveal their probable cytotoxic potential. The meristematic root tips of Allium cepa were used for testing the cytotoxic property of the aqueous leaf extracts containing both polar and non-polar compounds, and that containing polar compounds alone, at different concentrations (0.125, 0.25, 0.5, 1 and 2 %) and at different time durations, using distilled water as negative control. Mitotic squash preparations were made using a standard protocol. The mitotic index of the treated root tip cells was found to be decreasing and the abnormality percentage was found to be increasing with increase in extract concentration when compared with the control. Maximum cytotoxicity was observed in the extract containing both polar and non-polar compounds. Both the tested plants were found to be cytotoxic. The abnormalities noticed were of both clastogenic (nuclear lesions, nuclear fragmentation, etc.) and non-clastogenic (aberrant cell wall formation at cytokinesis, ball metaphase, etc.) types. Both plant extracts were found to significantly (P < 0.05) inhibit root growth of Allium cepa with an EC50 value of 1.92 % (R(2) = 0.594) for Hyptis and 1.58 % (R(2) = 0.757) for Leucas. The results were also analysed statistically by using analysis of variance followed by appropriate post hoc tests. These two weeds are aromatic plants comprising of essential oils that are volatile, natural complex compounds characterized by a strong odour and formed as secondary metabolites. In nature, essential oils play an important role in the protection of the plants as insecticides by reducing their appetite for such plants. When specifically targeted the concept of effectively exploiting these weeds for the formulation of herbal insecticides/pesticides may be possible in the near future.