Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity

Abstract

Precision medicine considers an individual’s unique physiological characteristics as strongly influential in disease vulnerability and in response to specific therapies. Predicting an individual’s susceptibility to developing an illness, making an accurate diagnosis, maximizing therapeutic effects, and minimizing adverse effects for treatment are essential in precision medicine. We introduced model-based precision medicine optimization approaches, including pathogenesis, biomarker detection, and drug target discovery, for treating presynaptic dopamine overactivity. Three classes of one-hit and two-hit enzyme defects were detected as the causes of disease states by the optimization approach of pathogenesis. The cluster analysis and support vector machine was used to detect optimal biomarkers in order to discriminate the accurate etiology from three classes of disease states. Finally, the fuzzy decision-making method was employed to discover common and specific drug targets for each classified disease state. We observed that more accurate diagnoses achieved higher satisfaction grades and dosed fewer enzyme targets to treat the disease. Furthermore, satisfaction grades for common drugs were lower than for specific ones, but common drugs could simultaneously treat several disease states that had different etiologies.

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…In drug discovery studies for schizophrenia, researchers have utilized AI/ML methods with various purposes, including drug target identification, 363,364 developing QSAR models, 365 predicting monitoring dosing compliance, 366 predicting GPCRs targeting compounds, 364 and drug repositioning 367 . Specifically, schizophrenia target genes were identified based on publicly available microarray data sets using an SVM‐RFE (recursive feature elimination)‐based feature selection, where the genes initially ranked by an SVM classifier and the signature was then identified by discarding the genes that were not differentially expressed.…”

“…Specifically, schizophrenia target genes were identified based on publicly available microarray data sets using an SVM‐RFE (recursive feature elimination)‐based feature selection, where the genes initially ranked by an SVM classifier and the signature was then identified by discarding the genes that were not differentially expressed. To detect optimal biomarkers of presynaptic dopamine overactivity, which may cause schizophrenia, an SVM classifier was used 363 . SVM classifiers were also used to predict QSAR models of the GABA (gamma aminobutyric acid) uptake inhibitor drugs, which can be beneficial in the treatment of schizophrenia 365 .…”

Clinical Physician Versus Data Scientist: Wearing Different Hats in Future Medicine

“…One example is precision medicine, which relies on extensive data analysis, such as radiomics and genomics, to tailor treatments instead of adopting a one-size-fits-all approach. Multiple medical domains, including radiation oncology, psychiatry, and infectious diseases, may also benefit from the structure of this framework (7)(8)(9).…”

“…On the other hand, data-driven applications are not limited to hospitals and clinics; for instance, telemonitoring systems, predictive models for personalized asthma attacks, and autonomous geriatric fall detection systems thrive outside the hospital (7,8). In addition, there are also some health-based apps and the Internet of Things (IoT) devices regarding telemedicine.…”

Revolution in Health Care: How Will Data Science Impact Doctor–Patient Relationships?

“…Moreover, precision medicine aims to provide a personalized recommendation of the optimal treatment for each patient, relying on the analysis of large heterogeneous datasets, including imaging, genomics, or various biological values extracted from electronic health records. This framework can be applied in many areas of medicine, such as radiation oncology ( 15 ), psychiatry ( 16 ), and infectious diseases ( 17 ). While these developing medical applications will require rigorous clinical validation, many should find their way into daily clinical practice over the next few decades (Figure 1 ).…”