Introduction: An Overview of AI in Oncology Drug Discovery and Development

Artificial intelligence (AI) has been termed the machine for the fourth industrial revolution. One of the main challenges in drug discovery and development is the time and costs required to sustain the drug development pipeline. It is estimated to cost over 2.6 billion USD and take over a decade to develop cancer therapeutics. This is primarily due to the high numbers of candidate drugs failing at late drug development stages. Many sizable pharmaceutical and biotech companies have made considerable investments in AI. This is primarily due to recent advancements in AI, which have displayed the possibility of rapid low-cost drug discovery and development. This overview provides a general introduction to AI in drug discovery and development. This chapter will describe the conventional oncology drug discovery pipeline and its associated challenges. Fundamental AI concepts are also introduced, alongside historical and modern advancements within AI and drug discovery and development. Lastly, the future potential and challenges of AI in oncology are discussed

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease

Background: Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis