Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves. METHODS: In this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency. RESULTS: The coefficient of determination (R2) of 0.945 and a mean absolute error (MAE) of 0.421 were achieved on the test set when using MLP for total degradation. Similarly, we achieved a R2 of 0.908 and MAE of 1.435 when predicting potency using the RF model. When physical stability measurements are included into the MLP model, the MAE of predicting TD decreases to 0.148. Using a similar strategy for potency prediction, the MAE decreases to 0.705 for the RF model. CONCLUSIONS: We conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.

Peptide Oligomerization Memory Effects and Their Impact on the Physical Stability of the GLP-1 Agonist Liraglutide.

Peptides and proteins commonly have complex structural landscapes allowing for transformation into a wide array of species including oligomers, aggregates, and fibrils. The formation of undesirable forms including aggregates and fibrils poses serious risks from the perspective of drug development and disease. Liraglutide, a GLP-1 agonist for the treatment of diabetes, is a conjugated peptide that forms oligomers that can be stabilized by pH and organic solvents. We have developed an analytical toolkit to overcome challenges inherent to Liraglutide's conjugated acyl chain and probed the impact its oligomers have on its physical stability. Our studies show that Liraglutide's oligomer states have significant and potentially detrimental impacts on its propensity to aggregate and form fibrils as well as its potency. Liraglutide delivered as a synthetic peptide is able to maintain its oligomerization state in dried lyophilized powders, acting as a memory effect from its synthetic process and purification. Through Liraglutide's oligomer memory effect, we demonstrate the importance and impact the process for synthetic peptides can have on drug development spanning from discovery to formulation development.

Cancer supportive care, improving the quality of life for cancer patients. A program evaluation report.

GOALS OF WORK: As medical care for cancer has become more specialized in diagnosis, treatment has become more technical and fragmented. In order to help cancer patients and their families, we developed a coordinated program called the Stanford Cancer Supportive Care Program (SCSCP) at the Center for Integrative Medicine at Stanford Hospital and Clinics. The Stanford Cancer Supportive Care Program was initiated in 1999 to provide support for cancer patients, addressing the need for improved physical and emotional well-being and quality of life. This paper is a program evaluation report. PATIENTS AND METHODS: The number of patient visits grew from 421 in 1999 to 6319 in 2002. This paper describes the utilization of the SCSCP program as assessed by 398 patient visit evaluations during a 9-week period, January 2002 to March 2002. During this time we collected attendance records with demographic data and anonymous questionnaires evaluating each program. Patients were asked to evaluate how the program helped them regarding increase of energy, reduction in stress, restful sleep, pain reduction, sense of hopefulness, and empowerment. MAIN RESULTS: Over 90% of the patients using the SCSCP felt there was benefit to the program. Programs were chosen based on a needs assessment by oncologists, nurse managers, social workers, and patients. Massage, yoga, and qigong classes had the highest number of participants. Qualitative data showed benefit for each program offered. CONCLUSIONS: This evaluation of a free cancer supportive care program initiated in a hospital outpatient setting provides initial evidence of patient satisfaction and improvement in quality of life.