Clinical trial NCT05377879: An Advanced Decision Support Tool for Personalized Medicine for IVF Using Modeling and Optimization
Official title: A Clinical Trial to Determine the Effectiveness of Using the Proposed Decision Support Tool for Each Patient's Customized Optimal Drug Dosage Profile
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: IVF
Lead sponsor: Stochastic Research Technologies LLC
Sponsor class: INDUSTRY
Collaborators: name: Akansha Hospital and Research Institute, India
First posted: 2022-05-17
Enrollment: 44
Sex: FEMALE
Minimum age (years): 20
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: OPT-IVF is a decision support tool which personalizes and optimizes dosage for each patient for each cycle.
Primary purpose: OTHER

Brief summary: Aim: A Clinical trial to determine the effectiveness of using the proposed decision support tool (OPTIVF) for each patient's customized optimal drug dosage profile. This will be a two-arm (in the ratio 1:3) clinical trial involving more than 80 participants; one arm will undergo superovulation using dosages predicted by the decision support tool while the other arm has undergone current standard treatment. The investigators will compare the outcomes of the two groups of participants in terms of the numbers and percentage of mature follicles retrieved at the end of each cycle, total FSH and HMG dosages used, and the number of required testing days for that cycle. The participants considered will include all ages, with and without PCOS, and low, average, and high responders.

Detailed description: This is a multi-site cohort with two arms (one arm for the intervention and one arm for the traditional approach) clinical trial involving more than 70 participants. The population size for the clinical trial was kept small because retrospective data for 170 patients is already collected each for comparing the two arms of the trial. Further, the data for 45 patients is there from the early small clinical trial we conducted in India.

The main site for this task is the Akansha Hospital, India, and all the analysis will be carried out at the Stochastic Research site. Dr. Urmila Diwekar will be an investigator from the Stochastic Research Technologies LLC, and Dr. Nayana Patel will be an investigator from Akansha Hospital and Research Institute.

In our study, the investigators will be using the participant's age and day three serum day AMH and FSH levels to decide the starting dose for the patient's cycle. The investigators will use the first two days of data collected (Follicular size distribution, estrogen levels) for that paticipant to determine the optimal dosage profile for the entire cycle for that participant with the help of the decision support tool OPT-IVF for this intervention in the clinical trial.

Primary and secondary outcomes will be measured.

Eligibility criteria: Inclusion Criteria:

* Women undergoing infertility treatment

Exclusion Criteria:

* No male participants

Primary outcomes:
measure: Number of follicles retrieved
timeFrame: At the end of cycle 1 (each cycle ranges from 10 to 12 days)
measure: Number of M2s
timeFrame: At the end of cycle 1(each cycle ranges from 10 to 12 days)
measure: No. of Very Good Embryos
timeFrame: At the end of cycle 1 (each cycle ranges from 10 to 12 days)
Secondary outcomes:
measure: Pregnancy
timeFrame: 9 to 12 days after the transfer
Other outcomes:
Locations:
city: Anand
country: India
facility: Akansha Hospital and Research Institute
state: Gujrat
Study officials:
affiliation: Stochastic Research Technologies/University of Illinois at Chicago
name: Urmila Diwekar, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Nisal A, Diwekar U, Bhalerao V. Personalized medicine for in vitro fertilization procedure using modeling and optimal control. J Theor Biol. 2020 Feb 21;487:110105. doi: 10.1016/j.jtbi.2019.110105. Epub 2019 Dec 3.
citation: Yenkie KM, Diwekar UM, Bhalerao V. Modeling the superovulation stage in in vitro fertilization. IEEE Trans Biomed Eng. 2013 Nov;60(11):3003-8. doi: 10.1109/TBME.2012.2227742. Epub 2012 Nov 15.
citation: Yenkie KM, Diwekar U. Uncertainty in clinical data and stochastic model for in vitro fertilization. J Theor Biol. 2015 Feb 21;367:76-85. doi: 10.1016/j.jtbi.2014.11.004. Epub 2014 Dec 4.