Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks

Machine learning models to accelerate the design of polymeric long-acting injectables

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.

Effect of Powdered Cellulose Nanofiber with Different Particle Sizes on the Physical Properties of Tablets Manufactured via Direct Compression

Direct compression is a tableting technique that involves a few steps in non-demanding manufacturing conditions. High strength and rapid disintegration of tablet formulations were previously achieved through the addition of cellulose nanofibers (CNFs), which have recently attracted attention as a high-performance biomass material. However, CNF addition results in greater variation in tablet weight and drug content, potentially due to differences in particle size between CNF and other additives. Herein, we used pulverized CNF to evaluate the effect of CNF particle size on the variation in tablet weight and drug content. Tablet formulations consisted of CNF with different particle sizes (approximately 100 µm [CNF100] and 300 µm [CNF300], at 0, 10, 30, or 50%), lactose hydrate, acetaminophen, and magnesium stearate. Ten powder formulations with different particle sizes and CNF concentrations were prepared; thereafter, the tablets were produced using a rotary tableting press with a compression force of 10 kN. The variation in weight and drug content as well as the tensile strength, friability, disintegration time, and drug dissolution of tablets were evaluated. CNF100 addition to the tablets reduced the weight and drug content variation to a greater extent than CNF300 addition. Using CNF300, we produced tablets of sufficient strength and short disintegration time. These properties were also achieved with CNF100 addition. Our findings suggest that adding CNF of small particle size to the tablet formulation can reduce the variation in weight and drug content while maintaining high strength and short disintegration time.

Advances in Nanomedicine Design: Multidisciplinary Strategies for Unmet Medical Needs

Globally, a rising burden of complex diseases takes a heavy toll on human lives and poses substantial clinical and economic challenges. This review covers nanomedicine and nanotechnology-enabled advanced drug delivery systems (DDS) designed to address various unmet medical needs. Key nanomedicine and DDSs, currently employed in the clinic to tackle some of these diseases, are discussed focusing on their versatility in diagnostics, anticancer therapy, and diabetes management. First-hand experiences from our own laboratory and the work of others are presented to provide insights into strategies to design and optimize nanomedicine- and nanotechnology-enabled DDS for enhancing therapeutic outcomes. Computational analysis is also briefly reviewed as a technology for rational design of controlled release DDS. Further explorations of DDS have illuminated the interplay of physiological barriers and their impact on DDS. It is demonstrated how such delivery systems can overcome these barriers for enhanced therapeutic efficacy and how new perspectives of next-generation DDS can be applied clinically.

Machine learning directed drug formulation development

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

In situ evaluation of spatiotemporal distribution of doxorubicin from Drug-eluting Beads in a tissue mimicking phantom

Understanding the intra-tumoral distribution of chemotherapeutic drugs is extremely important in predicting therapeutic outcome. Tissue mimicking gel phantoms are useful for studying drug distribution in vitro but quantifying distribution is laborious due to the need to section phantoms over the relevant time course and individually quantify drug elution. In this study we compare a bespoke version of the traditional phantom sectioning approach, with a novel confocal microscopy technique that enables dynamic in situ measurements of drug concentration. Release of doxorubicin from Drug-eluting Embolization Beads (DEBs) was measured in phantoms composed of alginate and agarose over comparable time intervals. Drug release from several different types of bead were measured. The non-radiopaque DC Bead™ generated a higher concentration at the boundary between the beads and the phantom and larger drug penetration distance within the release period, compared with the radiopaque DC Bead LUMI™. This is likely due to the difference of compositional and structural characteristics of the hydrogel beads interacting differently with the loaded drug. Comparison of in vitro results against historical in vivo data show good agreement in terms of drug penetration, when confounding factors such as geometry, elimination and bead chemistry were accounted for. Hence these methods have demonstrated potential for both bead and gel phantom validation, and provide opportunities for optimisation of bead design and embolization protocols through in vitro-in vivo comparison.

Neural Network Modeling of AChE Inhibition by New Carbazole-Bearing Oxazolones

Acetylcholine esterase (AChE) is one of the targeted enzymes in the therapy of important neurodegenerative diseases such as Alzheimer's disease. Many studies on carbazole- and oxazolone-based compounds have been conducted in the last decade due to the importance of these compounds. New carbazole-bearing oxazolones were synthesized from several carbazole aldehydes and p-nitrobenzoyl glycine as AChE inhibitors by the Erlenmeyer reaction in the present study. The inhibitory effects of three carbazole-bearing oxazolone derivatives on AChE were studied in vitro and the experimental results were modeled using artificial neural network (ANN). The developed ANN provided sufficient correlation between several dependent systems, including enzyme inhibition. The inhibition data for AChE were modeled by a two-layered ANN architecture. High correlation coefficients were observed between the experimental and predicted ANN results. Synthesized carbazole-bearing oxazolone derivatives inhibited AChE under in vitro conditions, and further research involving in vivo studies is recommended. An ANN may be a useful alternative modeling approach for enzyme inhibition.

Investigating the Release of a Hydrophobic Peptide from Matrices of Biodegradable Polymers: An Integrated Method Approach

The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems.

Artificial neural networks in evaluation and optimization of modified release solid dosage forms

Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

Solubility-modulated monolithic osmotic pump tablet for atenolol delivery

A method for the preparation of monolithic osmotic pump tablet was obtained by modulating atenolol solubility with acid. Tartaric acid was used as solubility promoter, sodium chloride as osmotic agent and polyvinyl pyrrolidone as retardant agent. Ethyl cellulose was employed as semipermeable membrane containing polyethylene glycol 400 as plasticizer. The formulation of atenolol monolithic osmotic pump tablet was optimized by orthogonal design and evaluated by similarity factor (f(2)). The optimal monolithic osmotic pump tablet was found to be able to deliver atenolol at the rate of approximate zero-order up to 24h, independent of release media and agitation rate. The approach of solubility-modulated by acid-alkali reaction might be used for the preparation of osmotic pump tablet of other poorly water-soluble drugs with alkaline or acid groups.