New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations

Nano-hybrid systems are products of interactions between organic and inorganic materials designed planned to develop drug delivery platforms that can be self-assembled. Poloxamine, commercially available as Tetronic®, is formed by blocks copolymers consisting poly (ethylene oxide) (PEO) (propylene (PPO) units arranged in a four-armed star shape. Structurally, Tetronics similar Pluronics®, with an additional feature they also pH-dependent due their central ethylenediamine unit. Laponite synthetic clay the form discs diameter approximately 25 nm thickness 1 nm. Both compounds biocompatible considered candidates for formation carrier systems. The objective explore associations Tetronic (T1304) LAP (Laponite) at concentrations 1–20% (w/w) 0–3% (w/w), respectively. Response surface methodology (RMS) two types machine learning (multilayer perceptron (MLP) support vector (SVM)) were used evaluate physical behavior β-Lapachone (β-Lap) solubility β-Lap (model low water) has antiviral, antiparasitic, antitumor, anti-inflammatory properties. results show adequate approach predict nanocarrier without presence LAP. Additionally, analysis performed SVM showed better (R2 > 0.97) terms data adjustment evaluation solubility. Furthermore, this work presents new classifying phase using ML. allows creation different T1304 pHs temperatures. strategies excellent assisting optimized development nano-hybrid platforms.