Multi-factorial treatment paradigm detection could be the answer to complex diseases: a case study of ALS
Kittel, Tyler Elizabeth
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Amyotrophic Lateral Sclerosis (ALS) is a debilitating neurodegenerative disease with no known cause or cure. Through a combination of an open market-space, scientific curiosity, and the humanitarian motivation to advance medicine, every treatment option imaginable has been attempted for this condition in the hopes of finding a cure. This analysis incorporates 5026 paired treatment-to-control data points of the G93A SOD1 mouse model, the most commonly tested ALS model, in an effort to organize the vast amount of published data in the field and evaluate which approaches are most promising for further experimentation. An ANOVA analysis was completed comparing nine different pathophysiological treatment approaches to ALS as a function of seven stages of disease progression throughout the entire lifespan of the mice. Treatment efficacy was evaluated based on how well the treatment improved three disease metrics compared to their own experimental control. Patterns emerged for overall disease benefit, as well as for the three modality assessments including onset delay, survival prolongment, and general health scores. Combination treatments that fit into more than one category also performed better than individual therapies later in life. Interestingly, most treatments were administered before disease onset, yet benefit was almost solely found post-onset. These results suggest that patients may benefit from a targeted pro-active combinatorial treatment approach to combat the multiple failed regulations and general homeostatic instability characteristic of ALS. Such a conclusion correlates with the trend towards complex personal medicine treatment plans in other multifactorial diseases.