A model-based analysis of phenytoin and carbamazepine toxicity treatment using binding-competition during hemodialysis

pH Gradient Liposomes Extract Protein Bound Amitriptyline in Peritoneal Dialysis-Exploratory Work

Poisoning is a significant cause of injury-related death worldwide. Dialysis is usually ineffective in removing the toxin once it has been absorbed because of drug protein binding and high volumes of distribution. In this work, we explore whether the addition of liposomes to peritoneal dialysate could extract protein bound amitriptyline. Liposomes were prepared using the thin film hydration method. In the in vitro experiment, 3 mL of 20% albumin with a concentration of 6000 nmol/L amitriptyline in a proprietary dialysis cartridge was dialysed against 125 mL of phosphate-buffered saline with and without 80 mg 1,2-dioleoyl-sn-glycero-3-phosphoglycerol (DOPG) liposomes. In the in vivo arm, peritoneal dialysis was undertaken in 6 rats with pH gradient liposome augmented dialysate after intravenous amitriptyline injection. Peritoneal blood flow was estimated by CO₂ extraction. Total amitriptyline extracted was compared to freely dissolved (non-protein bound) and total amitriptyline perfusing the membrane during the peritoneal dwell. Mean liposome size for DOPG and acidic centre pH gradient liposomes was 119 nm and 430 nm, respectively. In the in vitro experiment, more amitriptyline was extracted into the liposome containing dialysate than the control dialysate (40 +/- 2 nmol/L vs. 27 +/- 1 nmol/L). In the in vivo experiment, the total amitriptyline in dialysate was 5240 +/- 2750 nmol. Mean total free amitriptyline perfusing the peritoneal membrane was 93 +/- 46 nmol. Mean total blood amitriptyline perfusing the peritoneal membrane was 23,920 +/- 6920 nmol. Two of the six animals were excluded due to overestimation of peritoneal blood flow. This exploratory work suggests the addition of liposome nanoparticles to peritoneal dialysate extracted protein bound amitriptyline from blood.

Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology

We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide health problem, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.

Removal of Protein-Bound Uremic Toxins Using Binding Competitors in Hemodialysis: A Narrative Review

Removal of protein-bound uremic toxins (PBUTs) during conventional dialysis is insufficient. PBUTs are associated with comorbidities and mortality in dialysis patients. Albumin is the primary carrier for PBUTs and only a small free fraction of PBUTs are dialyzable. In the past, we proposed a novel method where a binding competitor is infused upstream of a dialyzer into an extracorporeal circuit. The competitor competes with PBUTs for their binding sites on albumin and increases the free PBUT fraction. Essentially, binding competitor-augmented hemodialysis is a reactive membrane separation technique and is a paradigm shift from conventional dialysis therapies. The proposed method has been tested in silico, ex vivo, and in vivo, and has proven to be very effective in all scenarios. In an ex vivo study and a proof-of-concept clinical study with 18 patients, ibuprofen was used as a binding competitor; however, chronic ibuprofen infusion may affect residual kidney function. Binding competition with free fatty acids significantly improved PBUT removal in pre-clinical rat models. Based on in silico analysis, tryptophan can also be used as a binding competitor; importantly, fatty acids or tryptophan may have salutary effects in HD patients. More chemoinformatics research, pre-clinical, and clinical studies are required to identify ideal binding competitors before routine clinical use.

Interference of anthocyanin extracted from black soybean coats on aflatoxin B1-human serum albumin in the binding process

The effect of the anthocyanin cyanidin-3-O-glucoside (C3G) and its main gastrointestinal metabolites (PCA and PGA) on the binding of AFB1 and HSA were studied via spectrometry. C3G is relatively stable in the gastric environment, and the intestinal environment promotes its metabolism into PCA and PGA. Binary fluorescence experiments showed that both AFB1 and C3G, including PCA and PGA, can react with HSA. AFB1, C3G and PCA can bind at site I and site II of HSA; PGA binds at site II. The presence of C3G/PCA/PGA inhibits the degree of quenching. C3G/PCA does not change the quenching mechanism; it is still static quenching; however, dynamic quenching occurs in the (AFB1-HSA)-PGA system. In addition, the apparent binding constant and number of binding sites of AFB1-HSA also diminish to different degrees. C3G and its metabolites (PCA and PGA) interfere with the interaction between AFB1 and HSA, and can reduce AFB1 transport at pH 7.4 in vitro.Abbreviations: C3G: cyanidin-3-O-glucoside; M: metabolite; PCA: protocatechuic acid; PGA: phloroglucinol aldehyde; AFB1: aflatoxin B1; HSA: human serum albumin.