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.

Fig 1. Roadmap of model-based optimization for precision medicine.

Subjective component: The symptoms are inquired about according to DSM-IV and ICP-10 diagnostic criteria for the diagnosis of possible presynaptic dopamine overactivity. Objective component and assessment: Dopamine level is measured by image studies. Then, a single biomarker must be tested for identifying Class 1 patients with α₁₃ and α₁₄ defects. Furthermore, three to four biomarkers are examined for identifying Class 2 or Class 3 patients with α₁₃ and α₁₅ or α₁₄ and α₁₅ defects, respectively. Finally, if the enzyme activity can be tested, the specific disease states with precise single or multiple enzyme defects are obtained. Plan: When the dopamine level is determined, the 1st-line common drugs can be used without further information concerning enzyme defects. The 1st-line common drugs are compatible with current medications because tests for enzyme defects are not clinically available. After single or multiple biomarkers are examined to identify Class 1, 2, and 3 patients, 2nd-line common drugs are utilized. The 2nd-line common drugs are more effective than are the 1st-line common drugs. When specific enzyme defects are detected, the specific drugs can be discovered to treat patients with the highest therapeutic effect, the lowest adverse effect, and the lowest drug dose. Hence, the specific drugs are indicated for use in patients who are refractory to 1st- and 2nd-line common drugs.

Fig 2. Flowchart for the modified algorithm for NHDE.

The core procedure of the NHDE algorithm is the evaluation and selection operation as shown in the second and third block diagram of the flowchart. The evaluation step is to solve each nonlinear programming (NLP) problem produced from the maximizing decision problem for each target candidate. The fitness should be accompanied by a penalty value for infeasible solutions. The fitness of each NLP problem is computed for selecting the better individuals in the population, and then to generate the next individuals. The migration operation of NHDE is used to help all individuals escape from this local cluster. This migration operation is performed only if the measure of population diversity fails to satisfy the desired tolerance.

Fig 3. Flowchart for the triple SVM classifiers.

50% of data from class 1, 2, and 3 were used for training. A parallel architecture comprising triple SVMs was employed. The rest 50% of data were used for testing. Each SVM solves a two-class problem defined by one information class. The “winner-takes-all” rule is used for the final decision. The winning class is the one corresponding to the SVM with the highest output.

Fig 4. Dysregulated ratios of one-hit and two-hit enzymes.

(a). single enzyme defect, (b). DAT and VMAT2 defects, (c). MAO and VMAT2 defects, (d). DAT and MAO defects. The enzyme activities of MAO and DAT decrease and the enzyme activity of VMAT2 increases. The combinations of two-enzyme dysregulation from the pairs of MAO, DAT, and VMAT2 cause an overexpression of various dopamine levels, namely 600, 700, 800, 900, 1000, 1100, and 1200. FC denotes the fold change between the disease state and the health state.

Fig 5. Hierarchical tree in dopamine disease level of 1200.

The presynaptic dopamine overactivity with different etiologies could be qualitatively divided into two groups: one is the one with the MAO (α15) defect, and the other is without the MAO defect. D1200α1314_1 denotes dopamine disease level of 1200 with DAT (α13) and VMAT2 (α14) defects, and we use 10 different combinations of two-enzyme dysregulation for cluster analysis.