Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Industrial accidents involving release of chemicals into the environment:Ecotoxicology

Major industrial accidents pose a serious threat to surrounding habitats. Each accident is unique in terms of pollutants released, pollutant concentrations and pollutant dispersal. The habitats receiving the pollutant(s) are also unique. These factors mean that assessing the environmental and ecological impact of any given pollution event will be complex. Case histories of the biological impact of chemicals released from industrial accidents are reviewed to determine how to assess ecotoxicity of pollutants involved.

Improving antimicrobial release kinetics from hydrophobic polymer implants via ion pairing to combat biomedical-device related infection

Purpose The aim of this study is to improve the drug release properties of antimicrobial agents from hydrophobic biomaterials using using an ion pairing strategy. In so doing antimicrobial agents may be eluted and maintained over a sufficient time period thereby preventing bacterial colonisation and subsequent biofilm formation on medical devices. Methods The model antimicrobial agent was chlorhexidine and the selected fatty acid counter ions were capric acid, myristic acid and stearic acid. The polymethyl methacrylate films were loaded with 2% of fatty acid:antimicrobial agent at the following molar ratios; 0.5:1M, 1:1M and 2:1M and thermally polymerized using azobisisobutyronitrile initiator. Drug release experiments were subsequently performed over a 3-month period and the mass of drug released under sink conditions (pH 7.0, 37oC) quantified using a validated HPLC-UV method. Results In all platforms, a burst of chlorhexidine release was observed over the initial 24-hour period. Similar release kinetics were observed between the formulations during the initial 28 days. However, as time progressed, the chlorhexidine baseline plateaued after 56 days whereas formulations containing the counterions appeared to continuously elute linearly with time. As can be observed in figure 1, the rank order of total chlorhexidine release in the presence of 0.5M fatty acid was myristic acid (40%) > capric acid (35%) > stearic acid (30%)> chlorhexidine baseline (15%). Conclusion The incorporation of fatty acids within the formulation significantly improved chlorhexidine solubility within both the monomer and the polymer and enhanced the drug release kinetics over the period of study. This is attributed to the greater diffusivity of chlorhexidine through PMMA in the presence of fatty acids. In th absence of fatty acids, chlorhexidine release was facilitated by dissolution of surface associated drug particles. This study has illustrated the ability of fatty acids to modulate chlorhexidine release from a model biomaterial through enhanced diffusivity. This strategy may prove advantageous for improved medical devices with enhanced resistance to infection.