Hormone Pulsatility Differences via Coarse Time-sampling

Period of Performance: 05/15/2000 - 04/30/2001


Phase 2 SBIR

Recipient Firm

Chaotic Dynamical Systems
Guilford, CT 06437
Principal Investigator


The primary objective is to determine minimal sampling frequencies and study durations required to significantly differentiate hormonal secretory patterns between clinically distinct cohorts, via Approximate entropy (ApEn) and cross-ApEn, quantifications of sequential irregularity and two-variable asynchrony, respectively, developed by the principal investigator. ApEn and cross-ApEn have been broadly applied within endocrinology, complementarily to pulse detection methods. However, typical protocols employed to generate data sets suitable for 'pulsatility analysis' have required 60-300 samples, rendering such studies largely research (rather than clinical) methods, due primarily to considerable assay expense. In Phase I, via reanalyses of three published hormonal studies, we realized dramatic reduction in the number of samples required to do discriminatory pulsatility analysis via ApEn. In Phase II we will augment these findings to determine the breadth of this reductive capability, utilizing ApEn and cross- ApEn applied to tumoral settings, diabetes, and changes and disorders associated with aging and reproductive capacity. Such reduction in sampling requirements holds the potential to move pulsatility analysis from a primarily research basis to a clinically applicable protocol, in appropriate contexts. Phase III will commercialize this approach via incorporation of ApEn software into existing hormonal administration systems, and collaborations with pharmaceutical companies who manufacture the studied hormones. PROPOSED COMMERCIAL APPLICATIONS: Software development of an ApEn program for application to general endocrine hormone secretion time-series; incorporation of ApEn software into existing hormonal administration systems. Collaborations with pharmaceutical companies who manufacture estrogen, GnRH agonists, gonadotropins, testosterone and insulin.