Computational pharmaceutics - A new paradigm of drug delivery

Advancements in membrane technology for efficient POME treatment: A comprehensive review and future perspectives

The treatment of POME related contamination is complicated due to its high organic contents and complex composition. Membrane technology is a prominent method for removing POME contaminants on account of its efficiency in removing suspended particles, organic substances, and contaminants from wastewater, leading to the production of high-quality treated effluent. It is crucial to achieve efficient POME treatment with minimum fouling through membrane advancement to ensure the sustainability for large-scale applications. This article comprehensively analyses the latest advancements in membrane technology for the treatment of POME. A wide range of membrane types including forward osmosis, microfiltration, ultrafiltration, nanofiltration, reverse osmosis, membrane bioreactor, photocatalytic membrane reactor, and their combinations is discussed in terms of the innovative design, treatment efficiencies and antifouling properties. The strategies for antifouling membranes such as self-healing and self-cleaning membranes are discussed. In addition to discussing the obstacles that impede the broad implementation of novel membrane technologies in POME treatment, the article concludes by delineating potential avenues for future research and policy considerations. The understanding and insights are expected to enhance the application of membrane-based methods in order to treat POME more efficiently; this will be instrumental in the reduction of environmental pollution.

Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery

A methodology based on Principal Component Analysis (PCA) and machine learning (ML) regression was developed in this study for predicting 5-aminosalicylic acid drug release from polysaccharide-coated formulation. The Raman method was used for collection of spectral data which were then used as inputs to the ML models for estimation of drug release. For ML modeling, we examined the predictive accuracy of three machine learning models-Elastic Net (EN), Group Ridge Regression (GRR), and Multilayer Perceptron (MLP)-for forecasting the release behavior of samples. The dataset, consisting of 155 data points with over 1500 spectral features, underwent preprocessing involving normalization, Principal Component Analysis (PCA) for dimensionality reduction, and outlier detection using Cook's Distance. Model hyperparameters were tuned using the Slime Mould Algorithm (SMA), and each model's performance was evaluated through k-fold cross-validation (k = 3). Assessment metrics, such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), emphasize the MLP model's exceptional performance. On the test set, MLP achieved an R² of 0.9989, notably higher than EN's R² of 0.9760 and GRR's R² of 0.7137. Additionally, MLP exhibited remarkably low test RMSE and MAE values at 0.0084 and 0.0067, respectively, in comparison to EN's RMSE of 0.0342 and MAE of 0.0267, as well as GRR's RMSE of 0.0907 and MAE of 0.0744. Parity plots and learning curves further validate MLP's predictive reliability, demonstrating close alignment between actual and predicted values and efficient learning with minimal overfitting. Consequently, the MLP model emerges as the most effective approach for this predictive task, offering a robust tool for accurately modeling complex spectral data. These findings underscore the robustness of the MLP model, providing a reliable and efficient approach for predicting drug release in polysaccharide-coated formulations, with implications for advancing colonic drug delivery systems.

Advancements in Targeted Therapies for Colorectal Cancer: Innovative Drug Formulation and Delivery Strategies

Colorectal cancer (CRC) is a leading cause of cancer-related mortality globally, with increasing incidence presenting significant treatment challenges. Traditional nontargeted therapies often result in high toxicity and limited efficacy, underscoring the need for improved treatment modalities. This review highlights recent advancements in drug delivery systems to enhance therapeutic outcomes for CRC. We examine innovative strategies, including computer-assisted pharmaceutical formulation, sustained-release matrices, and prodrugs, as well as targeted delivery mechanisms such as exosomes, liposomes, hydrogels, antibody-drug conjugates, and stimuli-responsive systems. These methodologies offer improved drug biodistribution, enhanced targeting of cancer cells, and reduced off-target effects, promising better clinical outcomes. Additionally, we discuss the development of novel formulations designed to optimize the delivery of therapeutic agents in advanced CRC. Ongoing clinical trials investigating these innovative systems signify a shift toward more effective patient treatment options. While challenges remain in the clinical application of these targeted therapies, continued research offers promising avenues for improving patient outcomes in CRC. This study aims to inform future strategies for managing this aggressive disease, ultimately enhancing survival rates and quality of life for affected individuals.

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatments such as; topical therapies, phototherapy, systemic immune modulators, and biologics aim to relieve symptoms and improve patient quality of life. However, challenges like adverse effects, high costs, and individual response variability persist. Thus, the need for novel anti-psoriatic drugs has led to the exploration of drug repurposing, an approach that identifies new applications for existing drugs. This method is in its early stages but has gained popularity across both public and private sectors. Furthermore, artificial intelligence (AI) integration is revolutionizing the healthcare industry by enhancing efficiency, delivery, and personalization. Machine learning and deep learning algorithms have significantly impacted drug discovery, repurposing, and designing new molecules or drug delivery carriers. Nanotechnology, in addition to AI, plays a pivotal role in targeting repurposed drugs via the topical route with suitable nanocarriers. This method overcomes challenges associated with oral delivery, such as systemic toxicities, slow onset of action, first-pass effect, and poor bioavailability. This review addresses the practice of repurposing existing drugs for managing psoriasis, discussing the challenges of conventional therapy and how the incorporation of nanotechnology and AI can overcome these hurdles, facilitating the discovery of anti-psoriatic drugs and presenting promising strategies for novel therapeutics. Additionally, it discusses the general benefits of drug repurposing compared to de novo drug development and the potential drawbacks of drug repurposing.

Integrating Quantitative Methods & Modeling and Analytical Techniques in Reverse Engineering; A Cutting-Edge Strategy in Complex Generic Development

Generic drugs are crucial for healthcare, offering affordable alternatives to brand-name drugs. Complex generics, with intricate ingredients, are gaining increasing importance in managing chronic conditions. However, prior to the regulatory market approval, they must demonstrate similarity in active ingredients, formulations, strength, and administration routes to ensure bioequivalence. The primary constraint lies in demonstrating bioequivalence with the innovator drug using traditional methods includes a lack of advanced technologies, and standardized protocols for analysing complex products. Given the multifaceted nature of these products, a single methodology may not suffice to establish in vitro/in vivo bioequivalence. Recognizing this, the USFDA conducts several workshops aiming advancement of complex generic drug product development. Notably, these efforts highlight the need to use Quantitative Methods and Modeling (QMM) approaches to support generic product development. QMM is a scientific approach used to analyze data and simulate drug development processes, ensuring safe, effective, and similar formulations of generic drugs using mathematical, statistical, and computational tools. QMM facilitates the design of formulations and processes, establishes a framework for in vivo BE studies, and suggests alternative ways to demonstrate BE. Appropriate utilization of the QMM approach can reduce the need for unwanted in vivo studies and bolster in vitro approaches for generic product development. Furthermore, use of orthogonal analytical techniques to characterize and decode innovator drugs can provide valuable insights into product attributes. Integrating this data into QMM enables the assessment of critical material attributes, or critical process parameters, thus demonstrating sameness. The combined application of QMM and analytical techniques not only supports regulatory decisions but also enhances the success rate of complex generic drug products.

Insight into the Precipitation Inhibition of Polymers within Cocrystal Formulations in Solution Using Experimental and Molecular Modeling Techniques

This study investigated the role of various polymers as precipitation inhibitors in solutions of flufenamic acid (FFA) and its cocrystals with theophylline (FFA-TP) and nicotinamide (FFA-NIC). Through a combination of NMR spectroscopy, molecular dynamics simulations, and nucleation studies using Crystal16, we evaluated the effects of polyethylene glycol (PEG), polyvinylpyrrolidone-vinyl acetate (PVP-VA), and soluplus (SOL), both individually and in combinations, on the nucleation, diffusion, and self-association of FFA molecules in solution. 1H NMR and DOSY measurements revealed that while PEG was highly effective in reducing molecular mobility, thus significantly delaying nucleation, PVP-VA facilitated nucleation by enhancing FFA diffusion and aggregation. SOL provided a balance, enhancing molecular mobility but maintaining a delayed nucleation effect, likely due to micellar encapsulation, as evidenced by line broadening in 1H NMR. Combination systems such as PVP-VA-PEG and PVP-VA-SOL showed synergistic effects, with PVP-VA-SOL proving particularly effective in inhibiting FFA nucleation across all systems. Molecular dynamics simulations supported these findings by highlighting changes in intermolecular interactions and aggregation tendencies in the presence of each polymer. This comprehensive analysis suggested that selecting appropriate polymeric excipients, or combinations thereof, can finely tune the nucleation behaviors of drug solutions, offering a strategic approach to optimizing the stability of supersaturated drug solutions.

AI-directed formulation strategy design initiates rational drug development

Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such an inefficient screening manner not only wastes research investments but also threatens the safety of clinical volunteers and patients. A design-oriented paradigm for formulation strategy determination is urgently needed to initiate rational drug development. Herein, we introduce FormulationDT, the first data-driven and knowledge-guided artificial intelligence (AI) platform for rational formulation strategy design. Learning from approved drug formulations, FormulationDT devised a comprehensive formulation strategy design system containing 12 decisions for both oral and injectable administration. Utilizing PU-Decide, our specialized partially supervised learning framework designed for positive-unlabeled (PU) scenarios, FormulationDT developed precise and interpretable classification models for each decision, achieving area under the receiver operating characteristic curve (ROC_AUC) scores ranging from 0.78 to 0.98, with an average above 0.90. Incorporating extensive domain knowledge, FormulationDT is now accessible through a user-friendly web platform (http://formulationdt.computpharm.org/). Moreover, FormulationDT demonstrates its value by showcasing its application in proteolysis targeting chimeras (PROTACs) and recent drug approvals. Overall, this study created the first approved drug formulation dataset and tailored the PU-Decide framework to develop a high-performance, interpretable, and user-friendly AI formulation strategy design platform, which holds promise for driving risk reduction and efficiency gains across the life cycle of drug discovery and development.

Therapeutic Role of Heterocyclic Compounds in Neurodegenerative Diseases: Insights from Alzheimer's and Parkinson's Diseases

Alzheimer's and Parkinson's are the most common neurodegenerative diseases (NDDs). The development of aberrant protein aggregates and the progressive and permanent loss of neurons are the major characteristic features of these disorders. Although the precise mechanisms causing Alzheimer's disease (AD) and Parkinson's disease (PD) are still unknown, there is a wealth of evidence suggesting that misfolded proteins, accumulation of misfolded proteins, dysfunction of neuroreceptors and mitochondria, dysregulation of enzymes, and the release of neurotransmitters significantly influence the pathophysiology of these diseases. There is no effective protective medicine or therapy available even with the availability of numerous medications. There is an urgent need to create new and powerful bioactive compounds since the number of people with NDDs is rising globally. Heterocyclic compounds have consistently played a pivotal role in drug discovery due to their exceptional pharmaceutical properties. Many clinically approved drugs, such as galantamine hydrobromide, donepezil hydrochloride, memantine hydrochloride, and opicapone, feature heterocyclic cores. As these heterocyclic compounds have exceptional therapeutic potential, heterocycles are an intriguing research topic for the development of new effective therapeutic drugs for PD and AD. This review aims to provide current insights into the development and potential use of heterocyclic compounds targeting diverse therapeutic targets to manage and potentially treat patients with AD and PD.

AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision

Transdermal drug delivery systems (TDDS) offer an alternative to conventional oral and injectable drug administration by bypassing the gastrointestinal tract and liver metabolism, improving bioavailability, and minimizing systemic side effects. However, widespread adoption of TDDS is limited by challenges such as the skin's permeability barrier, particularly the stratum corneum, and the need for optimized formulations. Factors like skin type, hydration levels, and age further complicate the development of universally effective solutions. Advances in artificial intelligence (AI) address these challenges through predictive modeling and personalized medicine approaches. Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. AI-driven algorithms optimize formulations, including penetration enhancers and advanced delivery technologies like microneedles and liposomes, while ensuring safety and efficacy. Personalized TDDS design tailors drug delivery to individual patient profiles, enhancing therapeutic precision. Innovative systems, such as sensor-integrated patches, dynamically adjust drug release based on real-time feedback, ensuring optimal outcomes. AI also streamlines the pharmaceutical process, from disease diagnosis to the prediction of drug distribution in skin layers, enabling efficient formulation development. This review highlights AI's transformative role in TDDS, including applications of models such as Deep Neural Networks (DNN), Artificial Neural Networks (ANN), BioSIM, COMSOL, K-Nearest Neighbors (KNN), and Set Covering Machine (SVM). These technologies revolutionize TDDS for both skin and non-skin diseases, demonstrating AI's potential to overcome existing barriers and improve patient care through innovative drug delivery solutions.

Screening Cyclodextrin Complexes for Bisphenols with High Binding Performance Based on the Data-Driven Model

The distinctive cavity structure of cyclodextrin, which results in binding properties, is credited with its application prospects in chemical, pharmacy, and material fields. The binding capacity can be regulated by substituting the hydroxyl groups on the cyclodextrins. It is possible to acquire anticipated binding properties by designing the modified groups on cyclodextrins. In this article, a data-driven model is proposed with a novel cyclodextrin/guest structure representation method to assist the cyclodextrin design. The model's performance is verified via several validations, as the squared correlation coefficients for cross-validation (Q2) and test set (R2test) are 0.801 and 0.841, respectively. With the proposed model and fluorescence experiments for cyclodextrin/bisphenol complexes, several cyclodextrin hosts, which have a strong binding capacity for bisphenols, are screened, synthesized, and characterized. The results show a controlled average absolute error of 0.605 M-1, suggesting the feasibility of data supplementation and molecular design. It is believed that the data-driven model can serve as theoretical assistance and a driving tool for the cyclodextrin complexes design, potentially leading to advancements in cyclodextrin's industrial applications and scientific research.

Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery

Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.

Nanomedicine in Ophthalmology: From Bench to Bedside

Ocular diseases such as cataract, refractive error, age-related macular degeneration, glaucoma, and diabetic retinopathy significantly impact vision and quality of life worldwide. Despite advances in conventional treatments, challenges like limited bioavailability, poor patient compliance, and invasive administration methods hinder their effectiveness. Nanomedicine offers a promising solution by enhancing drug delivery to targeted ocular tissues, enabling sustained release, and improving therapeutic outcomes. This review explores the journey of nanomedicine from bench to bedside, focusing on key nanotechnology platforms, preclinical models, and case studies of successful clinical translation. It addresses critical challenges, including pharmacokinetics, regulatory hurdles, and manufacturing scalability, which must be overcome for successful market entry. Additionally, this review highlights safety considerations, current marketed and FDA-approved nanomedicine products, and emerging trends such as gene therapy and personalized approaches. By providing a comprehensive overview of the current landscape and future directions, this article aims to guide researchers, clinicians, and industry stakeholders in advancing the clinical application of nanomedicine in ophthalmology.

Integrated computational approaches for advancing antimicrobial peptide development

The increasing prevalence of antimicrobial resistance has intensified the need for novel antimicrobial drugs. Antimicrobial peptides (AMPs) are promising alternative antibiotics due to their broad-spectrum activity and slower resistance development. However, the time-consuming, costly development and challenge of systematic optimization limit their translation into the clinic. Recently, integrating computational methods have led to breakthroughs in the precise design and optimization of AMPs, reduced resource consumption, and accelerated AMP development process. We highlight the application of these integrated approaches in AMP molecule discovery, optimization, and delivery and demonstrate the synergy of these strategies to fuel AMP development.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

Drug delivery from a ring implant attached to intraocular lens: An in-silico investigation

Multiple iterations required to design ocular implants, which will last for the desired operational period of months or even years, necessitate the use of in-silico models for ocular drug delivery. In this study, we developed an in-silico model to simulate the flow of Aqueous Humor (AH) and drug delivery from an implant to the Trabecular Meshwork (TM). The implant, attached to the side of the intraocular lens (IOL), and the TM are treated as porous media, with their effects on AH flow accounted for using the Darcy equation. This model accurately predicts the physiological values of Intraocular Pressure (IOP) for both healthy individuals and glaucoma patients, as reported in the literature. Results reveal that the effective diffusivity of the drug within the implant is the critical parameter that can alter the bioavailability time period (BTP) from a few days to months. Intuitively, BTP should increase as effective diffusivity decreases. However, we discovered that with lower levels of initial drug loading, BTP declines when effective diffusivity falls below a specific threshold. Our findings further reveal that, while AH flow has a minimal effect on the drug release profile at the implant site, it significantly impacts drug availability at the TM.

An optimized method for dose-effect prediction of traditional Chinese medicine based on 1D-ResCNN-PLS

We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.

Modeling on in vivo disposition and cellular transportation of RNA lipid nanoparticles via quantum mechanics/physiologically-based pharmacokinetic approaches

The lipid nanoparticle (LNP) has been so far proven as a strongly effective delivery system for mRNA and siRNA. However, the mechanisms of LNP's distribution, metabolism, and elimination are complicated, while the transportation and pharmacokinetics (PK) of LNP are just sparsely investigated and simply described. This study aimed to build a model for the transportation of RNA-LNP in Hela cells, rats, mice, and humans by physiologically based pharmacokinetic (PBPK) and quantum mechanics (QM) models with integrated multi-source data. LNPs with different ionizable lipids, particle sizes, and doses were modeled and compared by recognizing their critical parameters dominating PK. Some interesting results were found by the models. For example, the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids; the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release. Moreover, the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result. In summary, the transportation model of RNA LNP among various species for the first time was successfully constructed. Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling. The resulting new understandings are related to biodegradability, safety, and RNA release ability which are highly concerned issues of the formulation. This would benefit the design and research of RNA-LNP in the future.

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

A graph neural network approach for predicting drug susceptibility in the human microbiome

Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Modelling the controlled drug release of push-pull osmotic pump tablets using DEM

The push-pull osmotic pump tablet is a promising drug delivery approach, offering advantages over traditional dosage forms in achieving consistent and predictable drug release rates. In the current study, the drug release process of push-pull osmotic pump tablets is modelled for the first time using the discrete element method (DEM) incorporated with a microscopic diffusion-induced swelling model. The effects of dosage and formulation design, such as delivery orifice size, drug-to-polymer ratio, tablet surface curvature, friction between particles and cohesion of polymer particles, on the drug release performance are systematically analysed. Numerical results reveal that an enlarged delivery orifice significantly increases both the total drug release and the drug release rate. Moreover, the larger the swellable particle component in the tablet, the higher the drug release rate. Furthermore, the tablet surface curvature is found to affect the drug release profile, i.e. the final drug release percentage increases with the increasing tablet surface curvature. It is also found that the drug release rate could be controlled by adjusting the inter-particle friction and the cohesion of polymer particles in the formulation. This DEM study offers valuable insights into the mechanisms governing drug release in push-pull osmotic pump tablets.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Unveiling the synergy: a combined experimental and theoretical study of β-cyclodextrin with melatonin

Melatonin (MT) is a vital hormone controlling biorhythms, and optimizing its release in the human body is crucial. To address MT's unfavorable pharmacokinetics, we explored the inclusion complexes of MT with β-cyclodextrin (β-CD). Nano spray drying was applied to efficiently synthesize these complexes in three molar ratios (MT : β-CD = 1 : 1, 2 : 1, and 1 : 2), reducing reagent use and expediting inclusion. The complex powders were characterized through thermal analyses (TGA and DSC), Fourier transform infrared spectroscopy (FTIR), and in vitro MT release measurements via high-performance liquid chromatography (HPLC). In parallel, computational studies were conducted, examining the stability of MT : β-CD complexes by means of unbiased semi-empirical conformational searches refined by DFT, which produced a distribution of MT : β-CD binding enthalpies. Computational findings highlighted that these complexes are stabilized by specific hydrogen bonds and non-specific dispersive forces, with stronger binding in the 1 : 1 complex, which was corroborated by in vitro release data. Furthermore, the alignment between simulated and experimental FTIR spectra demonstrated the quality of both the structural model and computational methodology, which was crucial to enhance our comprehension of optimizing MT's release for therapeutic applications.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Oral delivery of posaconazole-loaded phospholipid-based nanoformulation: Preparation and optimization using design of experiments, machine learning, and TOPSIS

Phospholipid-based nanosystems show promising potentials for oral administration of hydrophobic drugs. The study introduced a novel approach to optimize posaconazole-loaded phospholipid-based nanoformulation using the design of experiments, machine learning, and Technique for Order of Preference by Similarity to the Ideal Solution. These approaches were used to investigate the impact of various variables on the encapsulation efficiency (EE), particle size, and polydispersity index (PDI). The optimized formulation, with %EE of ∼ 74 %, demonstrated a particle size and PDI of 107.7 nm and 0.174, respectively. The oral pharmacokinetic profiles of the posaconazole suspension, empty nanoformulation + drug suspension, and drug-loaded nanoformulation were evaluated. The nanoformulation significantly increased maximum plasma concentration and the area under the drug plasma concentration-time curve (∼3.9- and 6.2-fold, respectively) and could be administered without regard to meals. MTT and histopathological examinations were carried out to evaluate the safety of the nanoformulation and results exhibited no significant toxicity. Lymphatic transport was found to be the main mechanism of oral delivery. Caco-2 cell studies demonstrated that the mechanism of delivery was not based on an increase in cellular uptake. Our study represents a promising strategy for the development of phospholipid-based nanoformulations as efficient and safe oral delivery systems.

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles

Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.

Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities

The covalent modification of proteins with polymers is a well-established method for improving the pharmacokinetic properties of therapeutically valuable biologics. The conjugated polymer chains of the resulting hybrid represent highly flexible macromolecular structures. As the dynamics of such systems remain rather elusive for established experimental techniques from the field of protein structure elucidation, molecular dynamics simulations have proven as a valuable tool for studying such conjugates at an atomistic level, thereby complementing experimental studies. With a focus on new developments, this review aims to provide researchers from the polymer bioconjugation field with a concise and up to date overview of such approaches. After introducing basic principles of molecular dynamics simulations, as well as methods for and potential pitfalls in modeling bioconjugates, the review illustrates how these computational techniques have contributed to the understanding of bioconjugates and bioconjugation strategies in the recent past and how they may lead to a more rational design of novel bioconjugates in the future.

Ultrasound-mediated nano-sized drug delivery systems for cancer treatment: Multi-scale and multi-physics computational modeling

Computational modeling enables researchers to study and understand various complex biological phenomena in anticancer drug delivery systems (DDSs), especially nano-sized DDSs (NSDDSs). The combination of NSDDSs and therapeutic ultrasound (TUS), that is, focused ultrasound and low-intensity pulsed ultrasound, has made significant progress in recent years, opening many opportunities for cancer treatment. Multiple parameters require tuning and optimization to develop effective DDSs, such as NSDDSs, in which mathematical modeling can prove advantageous. In silico computational modeling of ultrasound-responsive DDS typically involves a complex framework of acoustic interactions, heat transfer, drug release from nanoparticles, fluid flow, mass transport, and pharmacodynamic governing equations. Owing to the rapid development of computational tools, modeling the different phenomena in multi-scale complex problems involved in drug delivery to tumors has become possible. In the present study, we present an in-depth review of recent advances in the mathematical modeling of TUS-mediated DDSs for cancer treatment. A detailed discussion is also provided on applying these computational models to improve the clinical translation for applications in cancer treatment. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Designing greener active pharmaceutical ingredients: Insights from pharmaceutical industry into drug discovery and development

Active pharmaceutical ingredients (APIs), their metabolites and transformation products (TPs) are found as pollutants in the environment. They can impact human and environmental health. To address this issue, an efficient, long-term prevention strategy could be the design of APIs that have less impact on the natural environment, i.e. the design of greener APIs, by the implementation of environmental parameters into the drug discovery and development process (also abbreviated R&D for 'research and development'). Our study aimed to evaluate the feasibility of the design of greener APIs based on insights from drug design experts working in large, research-based pharmaceutical companies. The feasibility evaluation also identified needs and incentives for process modification. For this purpose, 30 R&D and environmental experts from seven globally active pharmaceutical companies were interviewed along a structured questionnaire. Main findings are that the interviewed experts saw manifold opportunities to include properties rendering APIs greener in different stages along the R&D process. This implementation would be favoured by the fact that the pharmaceutical R&D process is very flexible and relies on balancing multiple parameters. Furthermore, some API properties that reduce environmental risks were considered compatible with common desirable properties for application. Environmental properties should be considered early during R&D, i.e. when molecules are screened and optimized. It has been found that availability of suitable in silico models and in vitro assays is crucial for this environmental consideration. Their attributes, e.g. throughput and costs, determine at which process stage they can be successfully applied. An intensified exchange between R&D and environmental experts within and outside companies would push the industrial application of the benign by design approach for APIs forward. Collaboration across pharmaceutical companies, authorities, and academia is seen as highly promising in this respect. Financial, social, and regulatory incentives would support future design of greener APIs.

Nanotechnological interventions in bacteriocin formulations - advances, and scope for challenging food spoilage bacteria and drug-resistant foodborne pathogens

Food spoilage bacteria (FSB) and multidrug-resistant (MDR) foodborne pathogens have emerged as one of the principal public health concerns in the twenty first century. The harmful effects of FSB lead to economic losses for the food industries. Similarly, MDR foodborne pathogens are accountable for multiple illnesses and pose a threat to consumers. Therefore, there is an urgent need to establish effective formulations for successful application against such microorganisms. In this context, the fusion of knowledge from biotechnology and nanotechnology can explore endless possibilities in the development of innovative formulations against FSB and foodborne pathogens. The current review critically examines the application of bacteriocins in the food industry and the use of nanomaterials to enhance the antimicrobial activity, stability, and precision in the target delivery of bacteriocins. This review also explores the technologies involved in the development of bacteriocin-based nanoformulations and their action against FSB and MDR foodborne pathogens, offering new possibilities in preservation technologies and addressing food safety issues in the food industry. The review highlights the challenges in the commercialization and technoeconomical feasibility of nanobacteriocin. Overall, it provides essential information and interpretation about nanotechnological advancements in bacteriocin formulation action against FSB and foodborne pathogens and future scopes.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Unravel the Tangle: Atomistic Insight into Ultrahigh Curcumin-Loaded Polymer Micelles

Amphiphilic ABA-triblock copolymers, comprised of poly(2-oxazoline) and poly(2-oxazine), can solubilize poorly water-soluble molecules in a structure-dependent manner forming micelles with exceptionally high drug loading. All-atom molecular dynamics simulations are conducted on previously experimentally characterized, curcumin-loaded micelles to dissect the structure-property relationships. Polymer-drug interactions for different levels of drug loading and variation in polymer structures of both the inner hydrophobic core and outer hydrophilic shell are investigated. In silico, the system with the highest experimental loading capacity shows the highest number of drug molecules encapsulated by the core. Furthermore, in systems with lower loading capacity outer A blocks show a greater extent of entanglement with the inner B blocks. Hydrogen bond analyses corroborate previous hypotheses: poly(2-butyl-2-oxazoline) B blocks, found experimentally to have reduced loading capacity for curcumin compared to poly(2-propyl-2-oxazine), establish fewer but longer-lasting hydrogen bonds. This possibly results from different sidechain conformations around the hydrophobic cargo, which is investigated by unsupervised machine learning to cluster monomers in smaller model systems mimicking different micelle compartments. Exchanging poly(2-methyl-2-oxazoline) with poly(2-ethyl-2-oxazoline) leads to increased drug interactions and reduced corona hydration; this suggests an impairment of micelle solubility or colloidal stability. These observations can help driving forward a more rational a priori nanoformulation design.

Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.

Emerging Applications of Nanotechnology in Healthcare and Medicine

Knowing the beneficial aspects of nanomedicine, scientists are trying to harness the applications of nanotechnology in diagnosis, treatment, and prevention of diseases. There are also potential uses in designing medical tools and processes for the new generation of medical scientists. The main objective for conducting this research review is to gather the widespread aspects of nanomedicine under one heading and to highlight standard research practices in the medical field. Comprehensive research has been conducted to incorporate the latest data related to nanotechnology in medicine and therapeutics derived from acknowledged scientific platforms. Nanotechnology is used to conduct sensitive medical procedures. Nanotechnology is showing successful and beneficial uses in the fields of diagnostics, disease treatment, regenerative medicine, gene therapy, dentistry, oncology, aesthetics industry, drug delivery, and therapeutics. A thorough association of and cooperation between physicians, clinicians, researchers, and technologies will bring forward a future where there is a more calculated, outlined, and technically programed field of nanomedicine. Advances are being made to overcome challenges associated with the application of nanotechnology in the medical field due to the pathophysiological basis of diseases. This review highlights the multipronged aspects of nanomedicine and how nanotechnology is proving beneficial for the health industry. There is a need to minimize the health, environmental, and ethical concerns linked to nanotechnology.

Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary

Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.

A computational-based approach to fabricate Ceritinib co-amorphous system using a novel co-former Rutin for bioavailability enhancement

In this study, we used molecular simulations to design Ceritinib (CRT) co-amorphous materials (CAMs) with concurrent improvement in solubility and bioavailability. Computational modeling enabled us to select the co-former by estimating the binding energy and intermolecular interactions. Rutin (RTH) was selected as a co-former for CRT CAMs using the solvent evaporation method to anticipate simultaneous improvement of solubility and bioavailability. The solid state characterization using DSC, XRPD, FT-IR, and a significant shift in Gordon Taylor experimental Tg values of co-amorphous materials revealed single amorphous phase formation and intermolecular interactions between CRT and RTH. The co-amorphous materials exhibited physical stability for up to 4 months under dry conditions (40 °C). Further, co-amorphous materials maintained the supersaturation for 24 hrs and improved solubility as well as dissolution of CRT. CRT:RTH 1:1 CAMs improved the permeability of CRT by 2 fold, estimated by employing the everted gut sac method. The solubility advantage of CAMs was also reflected in pharmacokinetic parameters, with a 3.1-fold and 2-fold improvement of CRT:RTH 2:1 in CRT exposure (AUC 0-t) and plasma concentration (Cmax) compared to the physical mixture, respectively.

Computational, In Vitro, and In Vivo Models for Nose-to-Brain Drug Delivery Studies

Direct nose-to-brain drug delivery offers the opportunity to treat central nervous system disorders more effectively due to the possibility of drug molecules reaching the brain without passing through the blood-brain barrier. Such a delivery route allows the desired anatomic site to be reached while ensuring drug effectiveness, minimizing side effects, and limiting drug losses and degradation. However, the absorption of intranasally administered entities is a complex process that considerably depends on the interplay between the characteristics of the drug delivery systems and the nasal mucosa. Various preclinical models (in silico, in vitro, ex vivo, and in vivo) are used to study the transport of drugs after intranasal administration. The present review article attempts to summarize the different computational and experimental models used so far to investigate the direct delivery of therapeutic agents or colloidal carriers from the nasal cavity to the brain tissue. Moreover, it provides a critical evaluation of the data available from different studies and identifies the advantages and disadvantages of each model.

A multi-step machine learning approach for accelerating QbD-based process development of protein spray drying

This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving the DoE models via multi-variate regression. This approach was followed as a benchmark to the ML approach. The more complex the process and required accuracy of the final model is, the more experiments are necessary. However, most biologics are expensive and thus experiments should be kept to a minimum. Therefore, the suitability of using a surrogate material and ML for the development of a DS was investigated. To this end, a DoE was performed with the surrogate and the data used for training the ML approach. The ML and DoE model predictions were compared to measurements of three protein-based validation runs. The suitability of using lactose as surrogate was investigated and advantages of the proposed approach were demonstrated. Limitations were identified at protein concentrations >35mg/ml and particle sizes of x₅₀>6µm. Within the investigated DS protein secondary structure was preserved, and most process settings, resulted in yields >75% and residual moisture <10wt.%.

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies

Low aqueous solubility is a common and serious challenge for most drug substances not only in development but also in the market, and it may cause low absorption and bioavailability as a result. Amorphization is an intermolecular modification strategy to address the issue by breaking the crystal lattice and enhancing the energy state. However, due to the physicochemical properties of the amorphous state, drugs are thermodynamically unstable and tend to recrystallize over time. Glass-forming ability (GFA) is an experimental method to evaluate the forming and stability of glass formed by crystallization tendency. Machine learning (ML) is an emerging technique widely applied in pharmaceutical sciences. In this study, we successfully developed multiple ML models (i.e., random forest (RF), XGBoost, and support vector machine (SVM)) to predict GFA from 171 drug molecules. Two different molecular representation methods (i.e., 2D descriptor and Extended-connectivity fingerprints (ECFP)) were implemented to process the drug molecules. Among all ML algorithms, 2D-RF performed best with the highest accuracy, AUC, and F1 of 0.857, 0.850, and 0.828, respectively, in the testing set. In addition, we conducted a feature importance analysis, and the results mostly agreed with the literature, which demonstrated the interpretability of the model. Most importantly, our study showed great potential for developing amorphous drugs by in silico screening of stable glass formers.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Nanocarriers for the Delivery of Neuroprotective Agents in the Treatment of Ocular Neurodegenerative Diseases

Retinal neurodegeneration is considered an early event in the pathogenesis of several ocular diseases, such as diabetic retinopathy, age-related macular degeneration, and glaucoma. At present, there is no definitive treatment to prevent the progression or reversal of vision loss caused by photoreceptor degeneration and the death of retinal ganglion cells. Neuroprotective approaches are being developed to increase the life expectancy of neurons by maintaining their shape/function and thus prevent the loss of vision and blindness. A successful neuroprotective approach could prolong patients' vision functioning and quality of life. Conventional pharmaceutical technologies have been investigated for delivering ocular medications; however, the distinctive structural characteristics of the eye and the physiological ocular barriers restrict the efficient delivery of drugs. Recent developments in bio-adhesive in situ gelling systems and nanotechnology-based targeted/sustained drug delivery systems are receiving a lot of attention. This review summarizes the putative mechanism, pharmacokinetics, and mode of administration of neuroprotective drugs used to treat ocular disorders. Additionally, this review focuses on cutting-edge nanocarriers that demonstrated promising results in treating ocular neurodegenerative diseases.