Verily Life Sciences LLC

University of California

California Institute of Technology

Sansum Diabetes Research Institute

Howard Zisser

Benyamin Grosman

Francis J Doyle, Iii

Eyal Dassau

Nikhil Roy

Joshua Burkart

Nikhil Bikhchandani

Shih-Yo Cheng

David A Tirrell

Lois Jovanovic

Rebecca Harvey

Seth Lieblich

Katharine Y Fang

Lois Javanovic

Francis J Doyle

Francis J Doyle Iii

Francis J Iii Doyle

Iii Francis J Doyle

To better understand the impact that meals have on the blood glucose level, some individuals with diabetes have begun recording meals consumed over time. However, the relationship between meals consumed by an individual and the glycemic health state of the individual has been difficult to understand. Introduced here are computer programs and associated computer-implemented techniques for utilizing information regarding the meals consumed by an individual to manage the blood glucose level of the individual in a personalized manner. By consistently and properly attributing physiological responses represented as excursions in blood glucose measurements to meals, the relationship between these meals and the glycemic health state can be better understood. For example, by examining this relationship, a diabetes management platform may be able to identify appropriate recommendations for monitoring, managing, and/or improving the glycemic health state of an individual.

Managing meal excursions in blood glucose data

Introduced here are diabetes management platforms able to guide people with diabetes toward a glycemic target. Rather than state the absolute amount of glucose within the blood, the diabetes management platform can instead produce personalized glycemic goals based on the physiological data associated with an individual. For example, if the diabetes management platform determines that the time spent within a first glycemic range exceeds a specified threshold, then the diabetes management platform may recommend that the individual attempt to keep their blood glucose level within a second glycemic range. Generally, the second glycemic range is closer than the first glycemic range to a target glycemic range corresponding to a healthy glycemic state.

Tiered time-in-range guiding interfaces for diabetes management

In some aspects, the invention relates to an insulin derivative, comprising a cyclic amino acid at position B28 or B29, wherein the cyclic amino acid is not L-proline. In some aspects, the invention relates to a method of making said insulin derivative. In some aspects, the invention relates to a pharmaceutical composition comprising said insulin derivative. In some aspects the invention relates to a method of treating a disease or condition in a subject comprising administering to the subject a composition comprising said insulin derivative.

Non-canonical insulins and their uses

A glucose rate increase detector (GRID) for use in an artificial pancreas (AP), wherein the GRID detects in a person persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the person to bolus for a meal, during open-loop control.

Glucose rate increase detector: a meal detection module for the health monitoring system

The disclosure relates to drug delivery and maintaining multiple defined physiological zones using model predictive control.

Maintaining multiple defined physiological zones using model predictive control

A method, computer implemented method and associated apparatus for the management of diabetes comprises utilizing zone model predictive control (Zone-MPC) to control delivery of an insulin or insulin analog within a zone of desired values.

Systems, devices and methods to deliver biological factors or drugs to a subject

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Santa Barbara, CA, United States of America

Howard Zisser