Decision Support System for Biotech Processes
Vaibhav Kumar (Prof. Munawar A. Shaik)
11 th October 2017, Wednesday, 04:30‐05:00 pm (25+5 mins), ChE Seminar hall
Biopharmaceuticals are therapeutic drugs driven from biological means such as monoclonal antibodies, engineered protein hormones, cytokines and vaccines. They are successful in curing autoimmune diseases by manipulating patient genetics with fewer side effects compared to traditional chemical drugs. As the biopharmaceutical sector has matured, pressure is increasing for design of flexible process configurations that can cope up process variability associated with the manufacturing of biopharmaceuticals. Due to biological nature of the reactants, it is challenging to maintain required purity of final product and other quality control measures. Under the risk of high clinical failure linked to drug development coupled with greater process variability, design of decision support system for multi-product facility in biopharma remains a challenge. Computer aided decision support systems have been used to assist decision making in the past for the biopharmaceutical process using heuristic approaches i.e. production planning and scheduling, facility design, debottlenecking and capacity analysis.
A lot of work has been done towards planning, scheduling and optimization in process system engineering (PSE), and recently increasing efforts are being made to understand process design and optimization in biopharmaceutical industry. More work is required to get clear, deep and better understanding about process design and optimization in this field. Therefore, the purpose of present study is to conduct research and develop novel models to fill the gap between computer aided process design tools and mathematical optimization and scheduling. An improved mixed integer linear programming (MILP) model has been formulated in this work for multi-stage, multi-product, mid-term planning of biopharmaceutical manufacturing, giving better results compared to literature.
Chromatography resins have limited lifetime and higher cost due to which it becomes necessary to get maximum output from purification step. Present work addresses maintenance scheduling under performance decay due to the decreasing efficiency of chromatography resins. In the present work an attempt is made to address economic analysis and process scheduling using conventional software tools (SuperPro & SchedulePro) vs mathematical programming to achieve significant economic benefits. Lethal Toxin Neutralizing Factor (LTNF) from E.coli for the treatment of snakebite, and batch and continuous processing of Granulocyte Colony –Stimulating Factor (GCSF) have been taken for the case study.