Topic: Medicine of the future Reading: Harbron, Chris (2006) Topic: Medicine of the future Reading: Harbron, Chris (2006). Statistics and the medicine of the future. Significance 3 (2), 66-68. Group 5: Shu Min, Yan Ling (Presenter), Yi Mou

Outline Development of medicine through the years Development of technologies for personalized medicine The role of statistics Challenges Future outlook

Development of medicine Water Retention

Development of medicine One pill has to fit all In trials of new drugs, when a small proportion react badly to them, the new drug will be rule out of use and unavailable to everyone

Personalized medicine?

Omics The pharmaceutical industry is looking into the development of a range of new technologies known as the “omics” Refers to a field of study in biology that ends in –omics, such as genomics or proteomics Aim to understand mechanisms of disease and examine cell processes at a very detailed molecular level

20,000 genes in the human genome Omics 20,000 genes in the human genome Genomic technology - identify associations of genes with any disease/drug responses Proteomic technology -identify the proteins that result in the progression of the disease 3 million different human protein species

Large datasets So how?

The role of statistics To organize, analyze and make inference from the data

Challenges Difficult to identify biases or outliers in the data of these small molecules Multivariate predictive modelling Efficiently process large quantities of data, adapt algorithms to cope with the size of the dataset

Challenges – Interpretation of results “With so many different analytes, whether they be genes, proteins or metabolites, some false positives of highly significant associations are likely to appear by chance.”

Challenges Comparisons for the testing of multiple hypothesis (Classical method: Bonferroni method) In medical testing, the false discovery rate is a more powerful test than the Bonferroni method. The false discovery rate accepts that you will select some differences between groups as interesting and assesses the quality of these differences and their likelihood of being genuine. Attach biological meaning to the statistical results

The future This area of personalized medicine has great potential Difficult to find new drugs that are safe and effective for all Development of omics technologies to ensure continuing improvements in medical treatment Technical and practical challenges of handling complex data, challenge of interpreting and attaching biological meaning to these results Collaboration of many disciplines

Thank you!

References http://www.statisticshowto.com/conservative/ https://www.fda.gov/downloads/ScienceResearch/SpecialTopics/Pers onalizedMedicine/UCM372421.pdf http://www.surveysystem.com/signif.htm https://www.ncbi.nlm.nih.gov/pubmed/24831050