Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery

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/.

Lipid-Based Nanoparticles for Drug/Gene Delivery: An Overview of the Production Techniques and Difficulties Encountered in Their Industrial Development

Over the past decade, the therapeutic potential of nanomaterials as novel drug delivery systems complementing conventional pharmacology has been widely acknowledged. Among these nanomaterials, lipid-based nanoparticles (LNPs) have shown remarkable pharmacological performance and promising therapeutic outcomes, thus gaining substantial interest in preclinical and clinical research. In this review, we introduce the main types of LNPs used in drug formulations such as liposomes, nanoemulsions, solid lipid nanoparticles, nanostructured lipid carriers, and lipid polymer hybrid nanoparticles, focusing on their main physicochemical properties and therapeutic potential. We discuss computational studies and modeling techniques to enhance the understanding of how LNPs interact with therapeutic cargo and to predict the potential effectiveness of such interactions in therapeutic applications. We also analyze the benefits and drawbacks of various LNP production techniques such as nanoprecipitation, emulsification, evaporation, thin film hydration, microfluidic-based methods, and an impingement jet mixer. Additionally, we discuss the major challenges associated with industrial development, including stability and sterilization, storage, regulatory compliance, reproducibility, and quality control. Overcoming these challenges and facilitating regulatory compliance represent the key steps toward LNP's successful commercialization and translation into clinical settings.

Technical and engineering considerations for designing therapeutics and delivery systems

The newly-emerged pathological conditions and increased rates of drug resistance necessitate application of the state-of-the-art technologies for accelerated discovery of the therapeutic candidates and obtaining comprehensive knowledge about their targets, action mechanisms, and interactions within the body including those between the receptors and drugs. Using the physics- and chemistry-based modern techniques for theranostic purposes, preparing smart carriers, local delivery of genes or drugs, and enhancing pharmaceutical bioavailability could be of great value against the hard-to-treat diseases and growing drug resistance. Besides the artificial intelligence- and quantum-based techniques, crystal engineering capable of designing new molecules with appropriate characteristics, improving the stability and bioavailability of poorly soluble drugs, and efficient carrier development could play a crucial role in manufacturing efficient pharmaceuticals and reducing the adverse events. In this context, identifying the structures and behaviors of crystals and predicting their characteristics are of great value. Electron diffraction by accelerated analysis of the chemicals and sensitivity to charge alterations, electromechanical tools for controlled delivery of therapeutics, mechatronics via fabrication of multi-functional smart products including the organ-on-chip devices for healthcare applications, and optomechatronics by overcoming the limitations of conventional biomedical techniques could address the unmet biomedical requirements and facilitate development of more effective theranostics with improved outcomes.

In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with R_val ²= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best R_val ² for both validation sets. For predictions that have high bias, improvement of R_val ² from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

Meta-Analysis: A Convenient Tool for the Choice of Nose-to-Brain Nanocarriers

OBJECTIVES

The intranasal route represents a high promising route of administration aiming for brain delivery. Yet, it represents one of the most difficult and complicated routes. Accordingly, scientists are in a continuous search for novel drug delivery vehicles such as the lipid and polymeric nanoparticles that are apt to enhance the bioavailability of the administered drugs to reach the brain. In this study, a certain number of publications were selected from different databases and literature. Meta-analysis studies using two different algorithms (DerSimonian-Laird and inverse variance) followed aiming to explore the published studies and confirm by evidence the superiority of nanocarriers in enhancing the brain bioavailability of various drugs. Furthermore, the quantitative comparison of lipid versus polymeric nanosystems was performed.

METHODS

The area under the curve (AUC) as an important pharmacokinetic parameter extracted from in vivo animal studies was designated as the "effect" in the performed meta-analysis after normalization. Forest plots were generated.

KEY FINDINGS AND CONCLUSIONS

The meta-analysis confirmed the augmentation of the AUC after the comparison with traditional preparations such as solutions and suspensions. Most importantly, lipid nanoparticles were proven to be significantly superior to the polymeric counterparts.

Construction of the small intestine on molecular dynamics simulation and preliminary exploration of drug intestinal absorption prediction

In this study, molecular dynamics simulation was applied to the construction of the small intestinal epithelial cell membrane and prediction of drug absorption. First, we constructed a system of a small intestinal epithelial cell membrane that was close to the real proportion and investigated the effects of temperature, water layer thickness, and ionic strength on membrane properties to optimize environmental parameters. Next, three drugs with different absorptivity, including Ephedrine (EPH), Quercetin (QUE), and Baicalin (BAI), were selected as model drugs to study the ability of drugs through the membrane by the free diffusion and umbrella sampling simulation, and the drug permeation ability was characterized by the free diffusion coefficient D and free energy barrier (△G) in the processes. The results showed that the free diffusion coefficient D and △G orders of the three drugs were consistent with the classical experimental absorption order, indicating that these two parameters could be used to jointly characterize the membrane permeability of the drugs.

Drug Carriers: A Review on the Most Used Mathematical Models for Drug Release

Carriers are protective transporters of drugs to target cells, facilitating therapy under each points of view, such as fast healing, reducing infective phenomena, and curing illnesses while avoiding side effects. Over the last 60 years, several scientists have studied drug carrier properties, trying to adapt them to the release environment. Drug/Carrier interaction phenomena have been deeply studied, and the release kinetics have been modeled according to the occurring phenomena involved in the system. It is not easy to define models’ advantages and disadvantages, since each of them may fit in a specific situation, considering material interactions, diffusion and erosion phenomena, and, no less important, the behavior of receiving medium. This work represents a critical review on main mathematical models concerning their dependency on physical, chemical, empirical, or semi-empirical variables. A quantitative representation of release profiles has been shown for the most representative models. A final critical comment on the applicability of these models has been presented at the end. A mathematical approach to this topic may help students and researchers approach the wide panorama of models that exist in literature and have been optimized over time. This models list could be of practical inspiration for the development of researchers’ own new models or for the application of proper modifications, with the introduction of new variable dependency.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Understanding the Effect of Functionalization on Loading Capacity and Release of Drug from Mesoporous Silica Nanoparticles: A Computationally Driven Study

MCM-41, a type of mesoporous silica nanoparticle, has garnered widespread interests as a useful carrier for drug delivery wherein the drug gets adsorbed into the pores of the carrier. To understand the adsorption mechanism and release of the drug at the molecular level, in the current study, it was attempted to generate a computational model for the loading of 5-fluorouracil (5-FU), a chemotherapeutic agent into surface-modified MCM-41. The molecular surface models of the mesoporous silica (MCM-41) nanoparticle with different surface substitutions were created. In the first stage, molecular mechanics (MM) simulations were carried out to obtain the optimized surface structures. Subsequently, a 5-FU drug molecule in its different forms was docked on top of different MCM-41 surfaces to understand the adsorption orientation and energetics. To further validate the results, more accurate quantum mechanical (QM) calculations were also carried out, and the energetics between the QM and MM calculations are found to be similar. All the substitutions (-NH₂, -CN, -COOH) except the methyl substitution exhibited favorable interactions compared to the unsubstituted MCM-41 surface which was in accordance with the experimental observations. The release rate of 5-FU from MCM-41 and aminopropyl-substituted MCM-41 (MCM-NH₂) was studied using molecular dynamics simulations which revealed that the release rate of 5-FU from the MCM-NH₂ surface was slower compared to that of plain MCM-41. The detailed surface characteristics and the adsorption energies from the molecular simulations correlating the loading capacity and release are reported in here.

Do Polymeric Nanoparticles Really Enhance the Bioavailability of Oral Drugs? A Quantitative Answer Using Meta-Analysis

The oral route remains one of the most popular and important routes of administration for drugs—one that warrants the development of advanced drug delivery systems, such as polymeric nanoparticles capable of enhancing the absorption and bioavailability of the used drugs. In this work, a systematic review of published works on several databases, followed by a meta-analysis, were utilized in order to navigate the published studies and access literature-based evidence about the capability of polymeric nanoparticulate systems to augment the absorption and bioavailability of orally administered drugs. The pharmacokinetic parameter of the area under the curve (AUC) was utilized as the “effect” of this meta-analytical study. The meta-analysis demonstrated a significant increase in AUC compared to conventional formulations. Furthermore, comparing the synthetic polymeric nanoparticles, versus their naturally-based administered counterparts, as subgroups of the meta-analysis, revealed no significant differences.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

Elaborated survey in the scope of nanocarriers engineering for boosting chemotherapy cytotoxicity: A meta-analysis study

Cancer is the prime cause of mortality throughout the world. Although the conventional chemotherapeutic agents damage the cancerous cells, they exert prominent injury to the normal cells owing to their lack of specificity. With advances in science, many research studies have been established to boost the cytotoxic effect of the chemotherapeutic agents via innovating novel nano-formulations having different variables. In the current meta-analysis study, combined data from different research articles were gathered for the evidence-based proof of the superiority of drug loaded nanocarriers over their corresponding conventional solutions in boosting the cytotoxic effect of chemotherapy in terms of IC50 values. The meta-analysis was subdivided into three subgroups; nanoparticles versus nanofibers, surface functionalized nanocarriers versus naked ones, and protein versus non-protein-based platforms. The different subgroups interestingly showed distinct scoring outcome data paving the road for cytotoxicity enhancement of the anti-cancer drugs in an evidence-based manner.

Synchronizing In Silico, In Vitro, and In Vivo Studies for the Successful Nose to Brain Delivery of an Anticancer Molecule

Sesamol is a sesame seed constituent with reported activity against many types of cancer. In this work, two types of nanocarriers, solid lipid nanoparticles (SLNs) and polymeric nanoparticles (PNs), were exploited to improve sesamol efficiency against the glioma cancer cell line. The ability of the proposed systems for efficient brain targeting intranasally was also inspected. By the aid of two docking programs, the virtual loading pattern inside these nanocarriers was matched to the real experimental results. Interactions involved in sesamol-carrier binding were also assessed, followed by a discussion of how different scoring functions account for these interactions. The study is an extension of the computer-assisted drug formulation design series, which represents a promising initiative for an upcoming industrial innovation. The results proved the power of combined in silico tools in predicting members with the highest sesamol payload suitable for delivering a sufficient dose to the brain. Among nine carriers, glyceryl monostearate (GMS) and polycaprolactone (PCL) scored the highest sesamol payload practically and computationally. The EE % was 66.09 ± 0.92 and 61.73 ± 0.47 corresponding to a ΔG (binding energy) of -8.85 ± 0.16 and -5.04 ± 0.11, respectively. Dynamic light scattering evidenced the formation of 215.1 ± 7.2 nm and 414.25 ± 1.6 nm nanoparticles, respectively. Both formulations demonstrated an efficient cytotoxic effect and brain-targeting ability compared to the sesamol solution. This was evidenced by low IC₅₀ (38.50 ± 10.37 μM and 27.81 ± 2.76 μM) and high drug targeting efficiency (7.64 ± 1.89-fold and 13.72 ± 4.1-fold) and direct transport percentages (86.12 ± 3.89 and 92.198 ± 2.09) for GMS-SLNs and PCL-PNs, respectively. The results also showed how different formulations, having different compositions and characteristics, could affect the cytotoxic and targeting ability.

Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.

Computational avenues in oral protein and peptide therapeutics

Proteins and peptides are amongst the most sought-after biomolecules because of their exceptional potential to cater to a vast range of diseases. Although widely studied and researched, the oral delivery of these biomolecules remains a challenge. Alongside formulation strategies, approaches to overcome the inherent barriers for peptide absorption are being designed at the molecular level to establish a sound rationale and to achieve higher bioavailability. Computer-aided drug design (CADD) is a modern in silico approach for developing successful bio-formulations. CADD enables intricate study of the biomolecules in conjunction with their target sites or receptors at the molecular level. Knowledge of the molecular interactions of proteins and peptides makes way for the pre-screening of suitable formulation components and facilitates their delivery.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Development of an RTD-Based Flowsheet Modeling Framework for the Assessment of In-Process Control Strategies

Continuous manufacturing (CM) is an emerging technology which can improve pharmaceutical manufacturing and reduce drug product quality issues. One challenge that needs to be addressed when adopting CM technology is material traceability through the entire continuous process, which constitutes one key aspect of control strategy. Residence time distribution (RTD) plays an important role in material traceability as it characterizes the material spreading through the process. The propagation of upstream disturbances could be predictively tracked through the entire process by convolution of the disturbance and the RTD. The present study sets up the RTD-based modeling framework in a commonly used process modeling environment, gPROMS, and integrates it with existing modules and built-in tools (e.g., parameter estimation). Concentration calculations based on the convolution integral requires access to historical stream property information, which is not readily available in flowsheet modeling platforms. Thus, a novel approach is taken whereby a partial differential equation is used to propagate and store historical data as the simulation marches forward in time. Other stream properties not modeled by an RTD are determined in auxiliary modules. To illustrate the application of the framework, an integrated RTD-auxiliary model for a continuous direct compression manufacturing line was developed. An excellent agreement was found between the model predictions and experiments. The validated model was subsequently used to assess in-process control strategies for feeder and material traceability through the process. Our simulation results show that the employed modeling approach facilitates risk-based assessment of the continuous line by promoting our understanding on the process.

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms

Cationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.

Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems

Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.

Effect of ligand conjugation site on the micellization of Bio-Targeted PLGA-Based nanohybrids: A computational biology approach

In this study, the effect of ligand binding position on the polymeric nanoparticles (NPs) is based on poly(lactic-co-glycolic acid) (PLGA) with two different polymer chain length at the atomistic level was presented. We explored the conjugation of riboflavin (RF) ligand from the end of the ribityl chain (N-10) to the polymer strands as well as from the amine group on the isoalloxazine head (N-3). The energy interactions for all samples revealed that the NPs containing ligands from N-10 positions have higher total attraction energies and lower stability in comparison with their peers conjugated from N-3. As NPs containing RF conjugated from N-3 exhibit the lower energy level with 20% and 10% of RF-containing composition for lower and higher. The introduction of RF from the N-10 position in any composition has increased the energy level of nanocarriers. The results of Gibb's free energy confirm the interatomic interaction energies trend where the lowest Gibbs free energy level for N-3 NPs occurs at 20 and 10% of RF-containing polymer content for PLGA10- and PLGA11- based NPs. Furthermore, with N-10 samples based on both polymers, non-targeted models form the stablest particles in each category. These findings are further confirmed with molecular docking analysis which revealed affinity energy of RF toward polymer chain from N-3 and N-10 are -981.57 kJ/mole and -298.23 kJ/mole, respectively. This in-silico study paves the new way for molecular engineering of the bio-responsive PLGA-PEG-RF micelles and can be used to nanoscale tunning of smart carriers used in cancer treatment.Communicated by Ramaswamy H. Sarma.

Comparing cefotaxime and ceftriaxone in combating meningitis through nose-to-brain delivery using bio/chemoinformatics tools

Bio/chemoinformatics tools can be deployed to compare antimicrobial agents aiming to select an efficient nose-to-brain formulation targeting the meningitis disease by utilizing the differences in the main structural, topological and electronic descriptors of the drugs. Cefotaxime and ceftriaxone were compared at the formulation level (by comparing the loading in gelatin and tripalmitin matrices as bases for the formation of nanoparticulate systems), at the biopharmaceutical level (through the interaction with mucin and the P-gp efflux pumps) and at the therapeutic level (through studying the interaction with S. pneumoniae bacterial receptors). GROMACS v4.6.5 software package was used to carry-out all-atom molecular dynamics simulations. Higher affinity of ceftriaxone was observed compared to cefotaxime on the investigated biopharmaceutical and therapeutic macromolecules. Both drugs showed successful docking on mucin, P-gp efflux pump and S. pneumoniae PBP1a and 2b; but ceftriaxone showed higher affinity to the P-gp efflux pump proteins and higher docking on mucin. Ceftriaxone showed less out-of-matrix diffusion and higher entrapment on the gelatin and the tripalmitin matrices. Accordingly, Ceftriaxone gelatin nanospheres or tripalmitin solid lipid nanoparticles may pose a more feasible and efficient nose-to-brain formulation targeting the meningitis disease compared to the cefotaxime counterparts.

Filgrastim loading in PLGA and SLN nanoparticulate system: a bioinformatics approach

OBJECTIVE

In this research work, we hypothesized to predict the nanoparticulate system, best suited for targeted delivery of filgrastim. Significance: Targeted delivery of filgrastim to bone marrow is required to decrease the incidence of neutropenia/febrile neutropenia. This is achieved by nanoparticulate systems, duly designed by bioinformatics approach.

METHOD

The targeted delivery of filgrastim in nanoparticulate system was achieved by molecular dynamics (MD) simulation studies. Two matrices comprising PLGA and SLN (tripalmitin, core component of SLN system) were modeled separately with proposed drug filgrastim. Energy minimization of all systems was done using the steepest descent method. PLGA and tripalmitin systems were equalized at 310 °C, at 1 bar pressure with Berendsen barostat for 200 ps using a v-rescale thermostat for 100 ps. Atomistic MD simulations of four model system and mass density of interacting systems were calculated.

RESULTS

The mass density maps of each nanoparticle system, that is, PLGA and tripalmitin showed an increase in density toward the end of the simulation. The contact numbers attained equilibria with the average number of approx.. 1500 contacts in case of tripalmitin-filgrastim system. While PLGA-filgrastim system shows lesser contacts as compared to tripalmitin with average contacts of approx. 1000.The binding free energy was predicted to be -1104 kJ/mol in tripalmitin-filgrastim complex and -421 kJ/mol in PLGA-filgrastim system.

CONCLUSION

Findings of study revealed that both nanoparticle systems assumed to be good model for drug-carrier systems. Though SLN systems were thought to be more appropriate than PLGA, still the in vivo findings could ascertain this hypothesis in futuristic work.

An update on the use of molecular modeling in dendrimers design for biomedical applications: are we using its full potential?

INTRODUCTION

Dendrimers are well-defined hyperbranched polymers built from a variety of different monomers and with tuneable properties that make them suitable for different biomedical applications. Their three-dimensional (3D) structure cannot be usually determined experimentally due to their inherent nature of repeating patterns in the topology, failure to crystalize, and/or high flexibility. Therefore, their conformations and interactions at the atomistic level can be studied only by using computational chemistry methods, including molecular dynamics, Monte Carlo simulations, and molecular docking.

AREAS COVERED

In this review, the methods that could be utilized in computer-aided dendrimer design are considered, providing a list of approaches to generate initial 3D coordinates and selected examples of applications of relevant molecular modeling methods.

EXPERT OPINION

Computational chemistry provides an invaluable set of tools to study dendrimers and their interactions with drugs and biological targets. There is a gap in the software development that is dedicated to study of these highly variable and complex systems that could be overcome by the integration of already established approaches for topology generation and open source molecular modeling libraries. Furthermore, it would be highly beneficial to collate already built 3D models of various dendrimers with corresponding relevant experimental data.

The Design of Poly(lactide-co-glycolide) Nanocarriers for Medical Applications

Polymeric biomaterials have found widespread applications in nanomedicine, and poly(lactide-co-glycolide), (PLGA) in particular has been successfully implemented in numerous drug delivery formulations due to its synthetic malleability and biocompatibility. However, the need for preconception in these formulations is increasing, and this can be achieved by selection and elimination of design variables in order for these systems to be tailored for their specific applications. The starting materials and preparation methods have been shown to influence various parameters of PLGA-based nanocarriers and their implementation in drug delivery systems, while the implementation of computational simulations as a component of formulation studies can provide valuable information on their characteristics. This review provides a critical summary of the synthesis and applications of PLGA-based systems in bio-medicine and outlines experimental and computational design considerations of these systems.

Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods

The delivery of drugs is a topic of intense research activity in both academia and industry with potential for positive economic, health, and societal impacts. The selection of the appropriate formulation (carrier and drug) with optimal delivery is a challenge investigated by researchers in academia and industry, in which millions of dollars are invested annually. Experiments involving different carriers and determination of their capacity for drug loading are very time-consuming and therefore expensive; consequently, approaches that employ computational/theoretical chemistry to speed have the potential to make hugely beneficial economic, environmental, and health impacts through savings in costs associated with chemicals (and their safe disposal) and time. Here, we report the use of computational tools (data mining of the available literature, principal component analysis, hierarchical clustering analysis, partial least squares regression, autocovariance calculations, molecular dynamics simulations, and molecular docking) to successfully predict drug loading into model drug delivery systems (gelatin nanospheres). We believe that this methodology has the potential to lead to significant change in drug formulation studies across the world.

Strategic Approaches for Colon Targeted Drug Delivery: An Overview of Recent Advancements

Colon targeted drug delivery systems have gained a great deal of attention as potential carriers for the local treatment of colonic diseases with reduced systemic side effects and also for the enhanced oral delivery of various therapeutics vulnerable to acidic and enzymatic degradation in the upper gastrointestinal tract. In recent years, the global pharmaceutical market for biologics has grown, and increasing demand for a more patient-friendly drug administration system highlights the importance of colonic drug delivery as a noninvasive delivery approach for macromolecules. Colon-targeted drug delivery systems for macromolecules can provide therapeutic benefits including better patient compliance (because they are pain-free and can be self-administered) and lower costs. Therefore, to achieve more efficient colonic drug delivery for local or systemic drug effects, various strategies have been explored including pH-dependent systems, enzyme-triggered systems, receptor-mediated systems, and magnetically-driven systems. In this review, recent advancements in various approaches for designing colon targeted drug delivery systems and their pharmaceutical applications are covered with a particular emphasis on formulation technologies.

Particulate Systems in the Enhancement of the Antiglaucomatous Drug Pharmacodynamics: A Meta-Analysis Study

Glaucoma is a very serious disease that can lead to blindness in severe cases. In an attempt to increase the efficacy of the drugs used in treating this disease, many particulate systems (micro/nano and lipid-based/nonlipid-based) have been exploited. In this study, the meta-analysis approach was implemented in order to explore the published studies and extract the literature-based evidence (proof-of-concept studies = 16) that the particulate systems increase the efficiency of the investigated intraocular pressure drugs as demonstrated by the increase in the area under effect curve. Comparison of micron particles versus nanoparticles on the one hand and lipid-based versus nonlipid-based on the other hand, as subgroups of the meta-analysis, was also included in the study where the latter comparison led to insignificant differences, whereas the former has proven the superiority of the nanoparticles over the micronized counterparts.

Exploring with Molecular Dynamics the Structural Fate of PLGA Oligomers in Various Solvents

This study focuses on the solvent effects that promote preferred solvated structures of polylactic-co-glycolic acid (PLGA) oligomers of molecular weight 278, 668, and 1449 u in ethyl acetate, water, and a mixture of both solvents. Our methodology consists of all-atom, explicit solvent molecular dynamics simulations for inspection of the solvated oligomer structures at ambient conditions. Parameters for the generalized Amber force field are developed in this work for the ethyl acetate liquid and the PLGA oligomers. Energetics, oligomer radius of gyration, end-to-end distance, orientational order parameter, flexibility coefficient, and backbone dihedral angles are reported along with a size scaling property yielding a power law for PLGA oligomers in each of the three solvents considered. It is found that the PLGA oligomer has two characteristic states identified by a set of extended structures and a set of collapsed structures, the former being energetically preferred in ethyl acetate and its mixture with water. The two types of PLGA structures occur in the three solvents and although they flip from one to the other in a sporadic fashion, in ethyl acetate, the extended structures may persist for more than 20 ns. The collapsed structures are significantly more frequent in water, occurring seldom in the mixed ethyl acetate-water solvent. PLGA is a biodegradable polymer approved for use in pharmaceutical and biomedical applications. Insights provided therein are of importance for the polymer aggregation process and its glassy state in condensed phases.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Nonspherical Nanoparticle Shape Stability Is Affected by Complex Manufacturing Aspects: Its Implications for Drug Delivery and Targeting

The shape of nanoparticles is known recently as an important design parameter influencing considerably the fate of nanoparticles with and in biological systems. Several manufacturing techniques to generate nonspherical nanoparticles as well as studies on in vitro and in vivo effects thereof have been described. However, nonspherical nanoparticle shape stability in physiological-related conditions and the impact of formulation parameters on nonspherical nanoparticle resistance still need to be investigated. To address these issues, different nanoparticle fabrication methods using biodegradable polymers are explored to produce nonspherical nanoparticles via the prevailing film-stretching method. In addition, systematic comparisons to other nanoparticle systems prepared by different manufacturing techniques and less biodegradable materials (but still commonly utilized for drug delivery and targeting) are conducted. The study evinces that the strong interplay from multiple nanoparticle properties (i.e., internal structure, Young's modulus, surface roughness, liquefaction temperature [glass transition (T_g ) or melting (T_m )], porosity, and surface hydrophobicity) is present. It is not possible to predict the nonsphericity longevity by merely one or two factor(s). The most influential features in preserving the nonsphericity of nanoparticles are existence of internal structure and low surface hydrophobicity (i.e., surface-free energy (SFE) > ≈55 mN m^-1 , material-water interfacial tension <6 mN m^-1 ), especially if the nanoparticles are soft (<1 GPa), rough (R_rms > 10 nm), porous (>1 m² g^-1 ), and in possession of low bulk liquefaction temperature (<100 °C). Interestingly, low surface hydrophobicity of nanoparticles can be obtained indirectly by the significant presence of residual stabilizers. Therefore, it is strongly suggested that nonsphericity of particle systems is highly dependent on surface chemistry but cannot be appraised separately from other factors. These results and reviews allot valuable guidelines for the design and manufacturing of nonspherical nanoparticles having adequate shape stability, thereby appropriate with their usage purposes. Furthermore, they can assist in understanding and explaining the possible mechanisms of nonspherical nanoparticles effectivity loss and distinctive material behavior at the nanoscale.

Enhanced Allicin Cytotoxicity on HEPG-2 Cells Using Glycyrrhetinic Acid Surface-Decorated Gelatin Nanoparticles

The cytotoxic potential of allicin was evaluated on different cancer cell lines, particularly, hepatic (HepG-2), breast (MCF-7), lung (A-549), and prostatic (PC-3), where allicin scored an IC₅₀ score of 19.26 μM on HepG-2. In order to increase the cell uptake, optimized allicin-loaded gelatin nanoparticles (GNPs) were prepared where the optimum formulation was surface-conjugated to glycyrrhetinic acid. GNPs were optimized using a D-optimal design. The optimum formulation had a particle size of 370.7 ± 6.78 nm and polydispersity index of 0.0363 ± 0.009 and 39.13 ± 2.38% of drug entrapment. The conjugation of the ligand, glycyrrhetinic acid with allicin-loaded GNPs, was confirmed utilizing ¹H NMR. Drug release profiles in the presence/absence of collagenase were obtained. Finally, a cytotoxicity study on HepG-2 was performed for the unconjugated and conjugated allicin-loaded GNPs scoring IC₅₀ of 10.95 and 5.046 μM, revealing two- and fourfold enhancements in allicin cytotoxicity, respectively. To our knowledge, the ligand-carrier pair, glycyrrhetinic acid-gelatin, was not explored before, and the developed system poses a successful liver cancer therapy.

Formulation of Antimicrobial Tobramycin Loaded PLGA Nanoparticles via Complexation with AOT

Tobramycin is a potent antimicrobial aminoglycoside and its effective delivery by encapsulation within nanoparticle carriers could increase its activity against infections through a combination of sustained release and enhanced uptake. Effective antimicrobial therapy against a clinically relevant model bacteria (Pseudomonas aeruginosa) requires sufficient levels of therapeutic drug to maintain a drug concentration above the microbial inhibitory concentration (MIC) of the bacteria. Previous studies have shown that loading of aminoglycoside drugs in poly(lactic-co-glycolic) acid (PLGA)-based delivery systems is generally poor due to weak interactions between the drug and the polymer. The formation of complexes of tobramycin with dioctylsulfosuccinate (AOT) allows the effective loading of the drug in PLGA-nanoparticles and such nanoparticles can effectively deliver the antimicrobial aminoglycoside with retention of tobramycin antibacterial function.

Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions among) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward the rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Nonlinear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in the training data set. The models were shown to be robust after the inclusion of these new formulations, in that the new inclusions did not significantly change model regression. This approach provides the first step toward the development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics.

Development of MART for the Rapid Production of Nanostructured Lipid Carriers Loaded with All-Trans Retinoic Acid for Dermal Delivery

All-trans retinoic acid (ATRA) has been regarded as a wonder drug for many dermatological complications; however, its application is limited due to the extreme irritation, and toxicity seen once it has sufficiently concentrated into the bloodstream from the skin. Thus, the present study was aimed to increase the entrapment of ATRA and minimize its transdermal permeation. ATRA incorporated within nanostructured lipid carriers (NLCs) were produced by a green and facile thin lipid-film based microwave-assisted rapid technique (MART). The optimization was carried out using the response surface methodology (RSM)-driven artificial neural network (ANN) coupled with genetic algorithm (GA). The liquid lipid and surfactants were seen to play a very crucial role culminating in the particle size (< 70 nm), zeta potential (< - 32 mV), and entrapment of ATRA (> 98%). ANN-GA-optimized NLCs required a minimal quantity of the surfactants, formed within 2 min and were stable for 1 year at different storage conditions. The optimized NLC-loaded creams showed a skin retention (ex vivo) to an extent of 87.42% with no detectable drug in the receptor fluid (24 h) in comparison to the marketed cream which released 47.32% (12 h) of ATRA. The results were in good correlation with the in vivo skin deposition studies. The NLCs were biocompatible and non-skin irritant based on the primary irritation index. In conclusion, the NLCs were seen to have a very high potential in overcoming the drawbacks of ATRA for dermal delivery and could be produced conveniently by the MART.

Lamotrigine encapsulated intra-nasal nanoliposome formulation for epilepsy treatment: Formulation design, characterization and nasal toxicity study

The purpose of this study was to develop lamotrigine nanoliposomes (LTG-NLs) for the treatment in seizures. The formulation was prepared using thin film hydration and rehydration method using the phospholipon 90 G, cholesterol and tween 80 as main ingredients. The nanoliposomes were optimized by plucket burman design (PBD) and response surface methodology (RSM) optimization techniques. The optimized LTGNLopt was further characterized for surface morphology, in-vitro release, stability study, confocal laser scanning microscopic (CLSM) study and naso toxicity study. The results showed that LTGNLopt shown nano size with high entrapment and drug release. The ex-vivo permeation study and confocal laser microscopy study confirmed the enhancement in permeation across the goat nasal mucosa. From the study, it was concluded that the independent variables used to optimize the NLs shown significant effect on the dependent variables and consider effective lipid carrier system for intranasal delivery.

Armamentarium of nanoscaled lipid drug delivery systems customized for oral administration: In silico docking patronage, absorption phenomenon, preclinical status, clinical status and future prospects

Poor drug solubility and bioavailability remain a significant and frequently encountered concern for pharmaceutical scientists. Nanoscaled lipid drug delivery systems (NSLDDS) have exhibited great potentials in oral delivery of poorly water-soluble drugs, primarily for lipophilic drugs, with several successful clinical products. In the past few years, we have find out that optimized composition of drug in lipid, surfactant, or mixture of lipid and surfactant omits the solubility, permeability and bioavailability issues, which are potential limitations for oral absorption of poorly water-soluble drugs. Lipids not only vary in structures and physiochemical properties, but also in their digestibility and absorption pathway; therefore selection of lipid excipients and dosage form has a pronounced effect on biopharmaceutical aspects of drug absorption and distribution both in vitro and in vivo. Therefore, in current critical review, a comprehensive overview of the different lipid based nanostructured drug delivery systems intended for oral administration has been presented. In addition, implication of in silico docking in designing of NSLDDS as well as mechanism of absorption of different lipid based nanoformulations through intestinal absorption window has also been offered. Moreover, attention has also been paid to NSLDDS that are currently undergoing preclinical or clinical analysis.

Molecular mechanism of polymer-assisting supersaturation of poorly water-soluble loratadine based on experimental observations and molecular dynamic simulations

Polymers have been usually used to retard nucleation and crystal growth in order to maintain supersaturation, yet their roles in inhibition of nucleation and crystal growth are poorly understood. In our work, the polymer-based supersaturation performances and molecular mechanisms of poorly aqueous soluble loratadine were investigated. Two common hydrophilic polymers (hydroxylpropylmethyl cellulose acetate succinate (HPMC-AS) and poly(vinylpyrrolidone-co-vinyl-acetate) (PVP-VA)) were used. It was found that HPMC-AS was a better polymer to prevent drug molecules from aggregation and to maintain the supersaturated state in solution than PVP-VA. The in vitro dissolution experiments showed that HPMC-AS solid dispersions had more rapid release at pH 4.5 and 6.8 media than PVP-VA solid dispersions under the un-sink condition. Moreover, molecular dynamic simulation results showed that HPMC-AS was more firmly absorbed onto a surface of the drug nanoparticles than PVP-VA due to bigger hydrophobic areas of HPMC-AS. Thereby, crystallization process of loratadine was inhibited in the presence of water to provide prolonged stability of the supersaturated state. In conclusion, polymers played a key role in maintaining supersaturation state of loratadine solid dispersions by strong drug-polymer interactions and the hydrophobic characteristic of polymers.

Curcumin or bisdemethoxycurcumin for nose-to-brain treatment of Alzheimer disease? A bio/chemo-informatics case study

The current study introduces a new idea of utilising several bio/chemoinformatics tools in comparing two bio-similar natural molecules viz. curcumin and bisdemethoxycurcumin (BDMC) in order to select a potential nose-to-brain remedy for Alzheimer disease. The comparison comprised several bio/chemo informatics tools. It encompassed all levels starting from loading the drug in a certain carrier; PLGA nanoparticles, to the biopharmaceutical level investigating the interaction with mucin and inhibition of P-gp blood-brain barrier efflux pumps. Finally, the therapeutic level was investigated by studying the interaction with pharmacological targets such as amyloid peptide plaques and cyclooxygenase2 enzyme responsible for the inflammatory reactions of the studied disease. The comparison revealed the superiority of curcumin over BDMC. Five new analogues were also hypothesised where diethoxybisdemethoxycurcumin was  recommended as a superior molecule. This work introduced the virtual utilisation of bio/chemo informatics tools as a reliable and economic alternative to the exhausting and resources-consuming wet-lab experimentation.