Fuzzy optimization for detecting enzyme targets of human uric acid metabolism

Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

Simple Summary

Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Abstract

The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Phosphoribosyl Pyrophosphate Amidotransferase Promotes the Progression of Thyroid Cancer via Regulating Pyruvate Kinase M2

Background

Pyruvate kinase is an enzyme that catalyzes the conversion of phosphoenolpyruvate and ADP to pyruvate and ATP in glycolysis and plays a role in regulating cell metabolism. It is reported that the activity of pyruvate kinase is increased in cancers. Phosphoribosyl amidotransferase (PPAT) is reported to be a crucial regulator for pyruvate kinase activity in lung cancer. However, its role in thyroid cancer remains largely unknown.

Materials and Methods

Immunohistochemical analysis and qRT-PCR were used to detect the expression of PPAT in thyroid cancer samples. Both gain-of-function and loss-of-function models were constructed in thyroid cancer cell lines and the biological functions of PPAT on cellular phenotypes were studied using CCK-8 assay and transwell assay in vitro, respectively. Then, Western blot was used to evaluate the change of PKM2 and downstream signal pathways after PPAT was overexpressed or knocked down.

Results

Immunohistochemical analysis showed increased expression of PPAT in thyroid cancer tissues, and it was associated with unfavorable pathological characteristics. Knockdown and overexpression assays suggested that altering PPAT expression modulated cell proliferation, migration, and invasion. In terms of mechanism, PPAT could positively regulate the expression of PKM2 and activate ERK and STAT3 signaling pathways.

Conclusion

PPAT plays crucial roles in regulating proliferation, migration, and invasion of thyroid cancer cells via activating PKM2, ERK, and STAT3.

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

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.

Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency

Model-based optimization approaches are valuable in developing new drugs for human metabolic disorders. The core objective in most optimal drug designs is positive therapeutic effects. In this study, we considered the effects of therapeutic, adverse, and target variation simultaneously. A fuzzy optimization method was applied to formulate a multiobjective drug design problem for detecting enzyme targets in the presynaptic dopamine metabolic network to remedy two types of enzymopathies caused by deficiencies of vesicular monoamine transporter 2 (VMAT2) and tyrosine hydroxylase (TH). The fuzzy membership approach transforms a two-stage drug discovery problem into a unified decision-making problem. We developed a nested hybrid differential evolution algorithm to efficiently identify a set of potential drug targets. Furthermore, we also simulated the effects of current clinical drugs for Parkinson’s disease (PD) in this model and tried to clarify the possible causes of neurotoxic and neuroprotective effects. The optimal drug design could yield 100% satisfaction grade when both therapeutic effect and the number of targets were considered in the objective. This scenario required regulating one to three and one or two enzyme targets for 50%–95% and 50%–100% VMAT2 and TH deficiencies, respectively. However, their corresponding adverse and target variation effect grades were less satisfactory. For the most severe deficiencies of VMAT2 and TH, a compromise design could be obtained when the effects of therapeutic, adverse, and target variation were simultaneously applied to the optimal drug discovery problem. Such a trade-off design followed the no free lunch theorem for optimization; that is, a more serious dopamine deficiency required more enzyme targets and lower satisfaction grade. In addition, the therapeutic effects of current clinical medications for PD could be enhanced in combination with new enzyme targets. The increase of toxic metabolites after treatment might be the cause of neurotoxic effects of some current PD medications.

Hypouricemic effect of allopurinol are improved by Pallidifloside D based on the uric acid metabolism enzymes PRPS, HGPRT and PRPPAT

Allopurinol is a commonly used medication to treat hyperuricemia and its complications. Pallidifloside D, a saponin glycoside constituent from the total saponins of Smilax riparia, had been proved to enhanced hypouricemic effect of allopurinol based on uric acid metabolism enzyme XOD. In this study, we evaluated whether Pallidifloside D (5mg/kg) enhanced hypouricemic effect of allopurinol (5mg/kg) related to others uric acid metabolism enzymes such as PRPS, HGPRT and PRPPAT. We found that, compared with allopurinol alone, the combination of allopurinol and Pallidifloside D significantly up-regulated HGPRT mRNA expression and down-regulated the mRNA expression of PRPS and PRPPAT in PC12 cells (all P<0.01). These results strongly suggest that hypouricemic effect of allopurinol are improved by Pallidifloside D via numerous mechanisms and our data may have a potential value in clinical practice in the treatment of gout and other hyperuricemic conditions.