Drug delivery: Experiments, mathematical modelling and machine learning

Boso, Daniela P.; Mascolo, Daniele Di; Santagiuliana, Raffaella; Decuzzi, Paolo; Schrefler, Bernhard A.

doi:10.1016/j.compbiomed.2020.103820

Cited by 19 publications

(10 citation statements)

References 34 publications

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“…Models with some similarities to those detailed herein are being used for successful medical applications (e.g. [6,38]), and extensions to clinical applications of drug delivery and evaluation of drug efficiency are also possible; initial works in these directions can be found in [4,10].…”

Section: Discussionmentioning

confidence: 99%

A continuum mechanical framework for modeling tumor growth and treatment in two- and three-phase systems

2021

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The growth and treatment of tumors is an important problem to society that involves the manifestation of cellular phenomena at length scales on the order of centimeters. Continuum mechanical approaches are being increasingly used to model tumors at the largest length scales of concern. The issue of how to best connect such descriptions to smaller-scale descriptions remains open. We formulate a framework to derive macroscale models of tumor behavior using the thermodynamically constrained averaging theory (TCAT), which provides a firm connection with the microscale and constraints on permissible forms of closure relations. We build on developments in the porous medium mechanics literature to formulate fundamental entropy inequality expressions for a general class of three-phase, compositional models at the macroscale. We use the general framework derived to formulate two classes of models, a two-phase model and a three-phase model. The general TCAT framework derived forms the basis for a wide range of potential models of varying sophistication, which can be derived, approximated, and applied to understand not only tumor growth but also the effectiveness of various treatment modalities.

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Section: Discussionmentioning

confidence: 99%

A continuum mechanical framework for modeling tumor growth and treatment in two- and three-phase systems

2021

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“…A powerful tool that has gained traction in recent years of pharmaceutical development is that of mathematical modeling and machine learning. , Researchers can use these strategies to correlate trends, optimize experimental parameters, and predict future outcomes using this computational approach to interpret their results. With an organized data set containing an array of input and output variables, traditional statistic methods such as principal component analysis (PCA) and orthogonal partial least squares (OPLS) can be applied with a computational software to identify statistically significant trends and intervariable relationships .…”

Section: Conclusion and Future Considerationsmentioning

confidence: 99%

Drug Delivery Systems for Localized Cancer Combination Therapy

Woodring

Graham-Gurysh

Bachelder

et al. 2023

ACS Appl. Bio Mater.

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With over 2 million cancer cases and over 600,000 cancer-associated deaths predicted in the U.S. for 2022, this life-debilitating disease continuously impacts the lives of people across the nation every day. Therapeutic treatment options for cancer have historically involved chemotherapies to eradicate tumors with cytotoxic mechanisms which can negatively affect the efficacy versus toxicity ratio of treatment. With a need for more directed and therapeutically active options, targeted small-molecule inhibitors and immunotherapies have since emerged to mitigate treatment-associated toxicities. However, aggressive tumors can employ a wide range of defense mechanisms to evade monotherapy treatment altogether, resulting in the recurrence of therapeutically resistant tumors. Therefore, many clinical routines have included combination therapy in which anticancer agents are combined to provide a synergistic attack on tumors. Even with this approach, maximizing the efficacy of cancer treatment is contingent upon the dose of drug that reaches the site of the tumor, so often therapy is administered at the site of a tumor via localized delivery platforms. Commonly used platforms for localized drug delivery include polymeric wafers, nanofibrous scaffolds, and hydrogels where drug combinations can be loaded and delivered synchronously. Attaining synergistic activity from these localized systems is dependent on proper material selection and fabrication methods. Herein, we describe these important considerations for enhancing the efficacy of cancer combination therapy through biodegradable, localized delivery systems.

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“…Artificial intelligence (AI) based on machine learning (ML) has been increasingly used in different areas of knowledge. For example, ML techniques and algorithms allow a new analysis alternative and have accelerated discoveries of materials and formulations in the pharmaceutical field [37][38][39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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

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Drug delivery: Experiments, mathematical modelling and machine learning

Cited by 19 publications

References 34 publications

A continuum mechanical framework for modeling tumor growth and treatment in two- and three-phase systems

A continuum mechanical framework for modeling tumor growth and treatment in two- and three-phase systems

Drug Delivery Systems for Localized Cancer Combination Therapy

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

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