Modern bioprocess development employs statistically optimized design of experiments (DOE) and regression modeling to find optimal bioprocess set points. Using modeling software, such as JMP Pro, it is possible to leverage artificial neural networks (ANNs) to improve model accuracy beyond the capabilities of regression models. Herein, we bridge the gap between a DOE skill set and a machine learning skill set by demonstrating a novel use of DOE to systematically create and evaluate ANN architecture using JMP Pro software. Additionally, we run a mammalian cell culture process at historical, one factor at a time, standard least squares regression, and ANN-derived set points. This case study demonstrates the significant differences between one factor at a time bioprocess development, DOE bioprocess development and the relative power of linear regression versus an ANN-DOE hybrid modeling approach.

Identifying and managing cell therapy variability can be a significant challenge for a company seeking to commercialize a new product. Failure to address this issue can lead to negative consequences such as delayed approval due to unsuccessful clinical investigations, or failed product lots that do not meet release criteria. Allogeneic cell therapies can be particularly prone to variability challenges due to the use of variable input material. In order to support the manufacturers of cell therapies, the FDA has identified two primary regulatory pathways (351 vs 361) that reflect the relative risk of the product. In this review, we will discuss criteria that separate the two potential regulatory pathways for cell therapy products in the USA. Also, we will discuss what aspects of manufacturing and clinical trial execution might introduce undesired variability that can derail the path towards licensure and commercialization, along with tools to minimize these potential sources of variability. ClinicalTrials.gov Identifier: NCT03347708 and NCT03955315 Lay Summary Therapies that utilize live cells as the active ingredient, known as cell therapies, are a promising approach to treating many diseases that cannot be addressed with traditional medicines. However, with this great potential comes specific challenges associated with cell therapy, including identifying the appropriate regulatory approval pathway, manufacturing it in a reproducible way, and successfully executing clinical trials. In this review, we describe potential sources of variability that can negatively impact the translation of a cell therapy, and ways to minimize those risks.

Low back pain (LBP) is a serious medical condition that affects a large percentage of the population worldwide. One cause of LBP is disc degeneration (DD), which is characterized by progressive breakdown of the disc and an inflamed disc environment. Current treatment options for patients with symptomatic DD are limited and are often unsuccessful, so many patients turn to prescription opioids for pain management in a time when opioid usage, addiction, and drug-related deaths are at an all-time high. In this paper, we discuss the etiology of lumbar DD and currently available treatments, as well as the potential for cell therapy to offer a biologic, non-opioid alternative to patients suffering from the condition. Finally, we present an overview of an investigational cell therapy called IDCT (Injectable Discogenic Cell Therapy), which is currently under evaluation in multiple double-blind clinical trials overseen by major regulatory agencies. The active ingredient in IDCT is a novel allogeneic cell population known as Discogenic Cells. These cells, which are derived from intervertebral disc tissue, have been shown to possess both regenerative and immunomodulatory properties. Cell therapies have unique properties that may ultimately lead to decreased pain and improved function, as well as curb the numbers of patients pursuing opioids. Their efficacy is best assessed in rigorous double-blinded and placebo-controlled clinical studies.