In vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, fertilization, and embryo transfer. Superovulation is a drug-induced method to enable multiple ovula- tion per menstrual cycle and key component towards a successful IVF cycle. Although there are the general guidelines for dosage, the dose is not optimized for each patient, and complications, such as overstimulation, can occur. To overcome the shortcomings of this general system, a mathematical procedure is developed based theory of particulate processes like crystallization. The procedure can provide a customized model of this stage regarding the size distribution of eggs (follicles/ oocytes) obtained per cycle as a function of the chemical interactions of the drugs used and the conditions imposed on the patient during the cycle, which provide a basis for predicting the possible outcome. This talk describes the theory, model, and the optimal control procedure for improving outcomes of IVF treatment for one of the four protocols used in real practice. The validation of the procedure is performed using clinical data from the patients previously undergone IVF cycles. Customized patient-specific model parameters are obtained by using initial two-day data for each patient. Subsequently, this model is used to predict the Follicle Size Distribution (FSD) for the remaining days of the cycle. This procedure was conducted for 49 patients. The results of the customized models are found to be closely matching with the observed FSD. These results thus validate the modeling approach and consequently its use for predicting the customized optimal drug dosage for each patient. Using the customized model and the optimized dosage, the FSD at the end of the cycle was determined. A small double-blind clinical trial was also conducted in India. The results from the trial show that the dosage predicted by using the model is 40% less than the suggestion made by the IVF clinicians. The testing and monitoring requirements for patients using optimized drug dosage is reduced by 72%. Work on the other three protocols and for patients in the USA is started and is showing promising results.
Dr. Urmila Diwekar is the president of the Vishwamitra Research, a non-profit research institute that she founded to pursue multidisciplinary research in the areas of Optimization under Uncertainty and Computer-aided Design applied to Energy, Environment, and Sustainability. From 2002-2004, she was a Professor in the Departments of Chemical Engineering, Bio-Engineering, and Industrial Engineering, and in the Institute for Environmental Science and Policy, at the University of Illinois at Chicago (UIC). She has made major contributions to research on batch distillation, advanced power systems,sustainability, environmental management, nuclear waste disposal, molecular modeling, pollution prevention, renewable energy systems, and biomedical engineering. Her recent work extended her work on stochastic modeling and optimization of particulate processes in chemical engineering to biomedical engineering especially to In-Vitro Fertilization. She is the author of more than 170 peer-reviewed research papers, and has given over 350 presentations and seminars, and has chaired numerous sessions in national and international meetings.She has been the principal advisor to 41 Ph.D. and M.S. students, and has advised 13 post-doctoral fellows and researchers. During the past ten years, her students have won 6 best student paper awards from various AIChE and INFORMS sections at their respective meetings. In November 2011, she received the Thiele award for outstanding contributions to chemical engineering, awarded by the Chicago chapter of AIChE. In 2015 she received one of the most prestigious awards of AIChE (an Institute award), the Energy and Sustainability award for leadership in research related to conventional energy, renewable energy, energy-water nexus, carbon capture and environmental control for energy, pollution prevention, and sustainability.