Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

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.

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations

AIM

This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations.

METHODOLOGY

Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets.

RESULTS

The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles.

CONCLUSION

This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.

In Vitro Release from Polymeric Core/Shell Nanoparticles through the Lens of Multiscale Modeling

The large number of studies involving nanoparticles for cancer therapy is due to their peculiar features: they protect loaded active molecules while extending circulation time and can extravasate from the blood flow to the tumor to deliver drugs directly in the target area. Mathematical modeling can provide a preliminary in silico exploration of design space to optimize an experimental activity that often relies on a trial-and-error approach. However, because of the characteristic size of these vectors (10-1000 nm), numerous phenomena of interest occur at different time and length scales, making a single modeling technique insufficient to fully characterize the system of interest. In this work we employed a multiscale modeling approach, which bridges the phenomena of interest across different scales, to study the in vitro release from polymeric core/shell nanoparticles for cancer therapy loaded with an active compound assembled as a hydrophobic ion pair. The "computational microscope" provided by molecular dynamics simulations was used to track drug molecules through the release process at an atomic scale. The outcomes suggested that the drug is mainly partitioned in the polymer and released as hydrophobic ion pair rather than a free molecule, and that the hydrophobic ion pair is preferentially partitioned in Tween 20 micelles in the release media. A model at macroscale, aimed at describing the release rate and elucidating the release mechanism, was developed according to the results from molecular simulations and validated against experimental data. The outcomes provided insights that are challenging to be obtained experimentally and which supported the development and validation of a release model at macroscale. Overall, the adopted multiscale approach corroborated the experimental findings and provided significant insights into the mechanisms of release.

Predicting Surface pH in Unbuffered Conditions for Acids, Bases, and Their Salts - A Review of Modeling Approaches and Their Performance

Dissolution of ionizable drugs and their salts is a function of drug surface solubility driven by the surface pH, i.e., the microenvironmental pH at the solid/liquid interface, which will deviate from bulk pH when there is an acid-base reaction occurring at the solid/liquid interface. In this work, we first present a brief overview of the modeling approaches available in the literature, classified according to the rate-determining step assumed in the dissolution process. In the second part, we present and evaluate the prediction performance of two different modeling approaches for surface pH. The first method relies only on thermodynamic equilibria, while the second method accounts for transport phenomena of charged compounds through the diffusional boundary layer using the Nernst - Planck equation. Model outcomes are compared with experimental data taken from the literature and obtained during this work. In terms of surface pH predictions, the models provide identical values for weak acids or weak bases. The models' outcomes for bases are in good agreement with experimental data in acidic conditions (bulk pH 1-4), while overpredictions are observed in the 5-7 bulk pH range in a system-dependent manner. Deviations can be related to the effect of surface dissolution (also referred to as surface reaction), which may become a controlling mechanism and slow the replenishment of the unionized drug at the surface of the crystal. Surface pH predictions for acids are generally in good agreement with experiments, with a slight underestimation for some drug examples, which could be related to errors in intrinsic solubility determination or to the assumption of thermodynamic equilibrium at the surface of the drug. A good agreement is also observed for salts with the thermodynamic model except for mesylate salts, suggesting that other phenomena, not currently included in the thermodynamic equilibrium model, may determine the surface pH.

Concentration-dependent mechanism of the binding behavior of ibuprofen to the cell membrane: A molecular dynamic simulation study

Ibuprofen is a commonly used drug for treating headaches, pain, and fever. The lipid bilayer is the primary and most important interface for drugs to interact with biological systems. However, the molecular interactions between ibuprofen and the cell membrane are not well understood. Our findings suggest that the interactions between ibuprofen and the bilayer involve multiple steps and depend on the concentration of the drug. At low concentrations of ibuprofen, it can bind to the surface of the lipid bilayer. The electrostatic and vdW energies of IBU-lipid at 0 ns of the simulation were -22.5 ± 3.2 and -5.9 ± 1.2 kj.mol-1 Fig. 2. In the following, the vdW energy of the IBU-lipid was increased by around -134.6 ± 3.7 kj.mol-1 whereas the electrostatic energy of the IBU-lipid was significantly decreased. This binding is facilitated by electrostatic and vdW interactions between ibuprofen and the head group of lipids. In the second step, ibuprofen is inserted into the lipid bilayer and positioned at the interface between the bilayer and the aqueous phase. In high concentrations of ibuprofen, it moved to the central region of the lipid bilayer. At this concentration, the physical and structural properties of the cell membrane change significantly. Results from the radial distribution function analysis indicate that at low concentrations, ibuprofen molecules are situated close to the head groups of phosphate groups. However, at high concentrations of ibuprofen, these molecules move to the inner side of the lipid bilayer. In addition, our findings indicate that at low concentrations of ibuprofen, these molecules did not significantly alter the physical properties of the cell membrane. In contrast, at high concentrations of ibuprofen, the physical parameters of the hydrocarbon tails, such as thickness, fluidity, and order, changed dramatically. APL parameter for POPC membrane increased slightly to 0.60 and 0.63 nm2 in the presence of low and high concentrations of ibuprofen molecules. The three-step interaction between ibuprofen and the lipid bilayer involves several events, such as the movement of ibuprofen molecules towards the central region of the lipid bilayer and the deformation and alteration of the structural and stability properties of the cell membrane. These effects are observed only at high concentrations of ibuprofen. It appears that the side effects of ibuprofen overdose are related to changes in the properties of the cell membrane and, subsequently, the function of membrane-anchored target proteins.

Evaluation of Encapsulation Potential of Selected Star-Hyperbranched Polyglycidol Architectures: Predictive Molecular Dynamics Simulations and Experimental Validation

Polymers, including non-linear copolymers, have great potential in the development of drug delivery systems with many advantages, but the design requires optimizing polymer-drug interactions. Molecular dynamics (MD) simulations can provide insights into polymer-drug interactions for designing delivery systems, but mimicking formulation processes such as drying is often not included in in silico studies. This study demonstrates an MD approach to model drying of systems comprising either hydrophilic tinidazole or hydrophobic clotrimazole drugs with amphiphilic hyperbranched copolyethers. The simulated drying protocol was critical for elucidating drug encapsulation and binding mechanisms. Experimentally, two polymers were synthesized and shown to encapsulate clotrimazole with up to 83% efficiency, guided by interactions with the hydrophobic core observed in simulations. In contrast, tinidazole is associated with surface regions, indicating capacity differences between drug types. Overall, this work highlights MD simulation of the drying process as an important tool for predicting drug-polymer complex behaviour. The modelled formulation protocol enabled high encapsulation efficiency and opened possibilities for the design of delivery systems based on computationally derived binding mechanisms. This demonstrates a computational-experimental approach where simulated drying was integral to elucidating interactions and developing optimized complexes, emphasizing the value of molecular modelling for the development of drug delivery formulations.

Colon-targeted delivery of polyphenols: construction principles, targeting mechanisms and evaluation methods

Polyphenols have received considerable attention for their promotive effects on colonic health. However, polyphenols are mostly sensitive to harsh gastrointestinal environments, thus, must be protected. It is necessary to design and develop a colon-targeted delivery system to improve the stability, colon-targeting and bioavailability of polyphenols. This paper mainly introduces research on colon-targeted controlled release of polyphenols. The physiological features affecting the dissolution, release and absorption of polyphenol-loaded delivery systems in the colon are first discussed. Simultaneously, the types of colon-targeted carriers with different release mechanisms are described, and colon-targeting assessment models that have been studied so far and their advantages and limitations are summarized. Based on the current research on polyphenols colon-targeting, outlook and reflections are proposed, with the goal of inspiring strategic development of new colon-targeted therapeutics to ensure that the polyphenols reach the colon with complete bioactivity.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

COSMO Models for the Pharmaceutical Development of Parenteral Drug Formulations

The aqueous solubility of active pharmaceutical ingredients is one of the most important features to be considered during the development of parenteral formulations in the pharmaceutical industry. Computational modelling has become in the last years an integral part of pharmaceutical development. In this context, ab initio computational models, such as COnductor-like Screening MOdel (COSMO), have been proposed as promising tools for the prediction of results without the effective use of resources. Nevertheless, despite the clear evaluation of computational resources, some authors had not achieved satisfying results and new calculations and algorithms have been proposed over the years to improve the outcomes. In the development and production of aqueous parenteral formulations, the solubility of Active Pharmaceutical Ingredients (APIs) in an aqueous and biocompatible vehicle is a decisive step. This work aims to study the hypothesis that COSMO models could be useful in the development of new parenteral formulations, mainly aqueous ones.

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.

An Engineered Design of Self-Assembly Nanomedicine Guided by Molecular Dynamic Simulation for Photodynamic and Hypoxia-Directed Therapy

To overcome the hypoxia barrier in tumor therapy, a hypoxia-activated prodrug of docetaxel (DTX-PNB) was synthesized and self-assembled with indocyanine green (ICG), forming a combination nanomedicine ISDNN. With the guidance of molecular dynamic simulation, the ISDNN construction could be accurately controlled, achieving uniform size distribution and high drug loading up to 90%. Within the hypoxic tumor environment, ISDNN exerted ICG-mediated photodynamic therapy and aggravated hypoxia to boost DTX-PNB activation for chemotherapy, enabling enhanced antitumor efficacy.

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

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

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential to enhance drug bioavailability whilst reducing off-target effects. Delivering drugs to the colon requires considered formulation development, as both oral and rectal dosage forms can encounter challenges if the colon's distinct physiological environment is not appreciated. As the therapeutic opportunities surrounding colonic drug delivery multiply, the success of novel pharmaceuticals lies in their design. This review provides a modern insight into the key parameters determining the effective design and development of colon-targeted medicines. Influential physiological features governing the release, dissolution, stability, and absorption of drugs in the colon are first discussed, followed by an overview of the most reliable colon-targeted formulation strategies. Finally, the most appropriate in vitro, in vivo, and in silico preclinical investigations are presented, with the goal of inspiring strategic development of new colon-targeted therapeutics.

Perspective: New directions in dynamical density functional theory

Classical dynamical density functional theory (DDFT) has become one of the central modeling approaches in nonequilibrium soft matter physics. Recent years have seen the emergence of novel and interesting fields of application for DDFT. In particular, there has been a remarkable growth in the amount of work related to chemistry. Moreover, DDFT has stimulated research on other theories such as phase field crystal models and power functional theory. In this perspective, we summarize the latest developments in the field of DDFT and discuss a variety of possible directions for future research.

Coarse-Grained Simulations of Release of Drugs Housed in Flexible Nanogels: New Insights into Kinetic Parameters

The diffusion-controlled release of drugs housed in flexible nanogels has been simulated with the help of a coarse-grained model that explicitly considers polymer chains. In these in silico experiments, the effect of its flexibility is assessed by comparing it with data obtained for a rigid nanogel with the same volume fraction and topology. Our results show that the initial distribution of the drug can exert a great influence on the release kinetics. This work also reveals that certain surface phenomena driven by steric interactions can lead to apparently counterintuitive behaviors. Such phenomena are not usually included in many theoretical treatments used for the analysis of experimental release kinetics. Therefore, one should be very careful in drawing conclusions from these formalisms. In fact, our results suggest that the interpretation of drug release curves in terms of kinetic exponents (obtained from the Ritger-Peppas Equation) is a tricky question. However, such curves can provide a first estimate of the drug diffusion coefficient.

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

Predicting Bile and Lipid Interaction for Drug Substances

Predicting biopharmaceutical characteristics and food effects for drug substances may substantially leverage rational formulation outcomes. We established a bile and lipid interaction prediction model for new drug substances and further explored the model for the prediction of bile-related food effects. One hundred and forty-one drugs were categorized as bile and/or lipid interacting and noninteracting drugs using ¹H nuclear magnetic resonance (NMR) spectroscopy. Quantitative structure-property relationship modeling with molecular descriptors was applied to predict a drug's interaction with bile and/or lipids. Bile interaction, for example, was indicated by two descriptors characterizing polarity and lipophilicity with a high balanced accuracy of 0.8. Furthermore, the predicted bile interaction correlated with a positive food effect. Reliable prediction of drug substance interaction with lipids required four molecular descriptors with a balanced accuracy of 0.7. These described a drug's shape, lipophilicity, aromaticity, and hydrogen bond acceptor capability. In conclusion, reliable models might be found through drug libraries characterized for bile interaction by NMR. Furthermore, there is potential for predicting bile-related positive food effects.

Mining therapeutic targets from the antibiotic-resistant Campylobacter coli and virtual screening of natural product inhibitors against its riboflavin synthase

Campylobacter coli resides in the intestine of several commonly consumed animals, as well as water and soil. It leads to campylobacteriosis when humans eat raw/undercooked meat or come into contact with infected animals. A common manifestation of the infection is fever, nausea, headache, and diarrhea. Increasing antibiotic resistance is being observed in this pathogen. The increased incidence of C. coli infection, and post-infection complications like Guillain-Barré syndrome, make it an important pathogen. It is essential to find novel therapeutic targets and drugs against it, especially with the emergence of antibiotic-resistant strains. In the current study, genomes of 89 antibiotic-resistant strains of C. coli were downloaded from the PATRIC database. Potent drug targets (n = 36) were prioritized from the core genome (n = 1,337 genes) of this species. Riboflavin synthase was selected as a drug target and pharmacophore-based virtual screening was performed to predict its inhibitors from the NPASS (n =  ~ 30,000 compounds) natural product library. The top three docked compounds (NPC115144, NPC307895, and NPC470462) were selected for dynamics simulation (for 50 ns) and ADMET profiling. These identified compounds appear safe for targeting this pathogen and can be further validated by experimental analysis before clinical trials.

In silico identification of novel allosteric inhibitors of dengue virus NS2B/NS3 serine protease

Although dengue is a disease that affects more than 100 countries and puts almost 400 million lives at risk each year, there is no approved antiviral in the treatment of this pathology. In this context, proteases are potential biological targets since they are essential in the replication process of this virus. In this study, a library of more than 3,000 structures was used to explore the allosteric inhibition of the NS2B/NS3 protease complex using Consensual Docking techniques. The results show four best ranked structures that were selected for molecular dynamics and free energy simulations. Our analysis corroborate with other studies (experimental and theoretical) presented in the literature. Thus, the computational approach used here proved to be useful for planning new inhibitors in the combat against Dengue disease.

In silico investigation and surmounting of Lipopolysaccharide barrier in Gram-Negative Bacteria: How far has molecular dynamics Come?

Lipopolysaccharide (LPS), a main component of the outer membrane of Gram-negative bacteria, has crucial implications on both antibiotic resistance and the overstimulation of the host innate immune system. Fighting against these global concerns calls for the molecular understanding of the barrier function and immunostimulatory ability of LPS. Molecular dynamics (MD) simulations have become an invaluable tool for uncovering important findings in LPS research. While the reach of MD simulations for investigating the immunostimulatory ability of LPS has been already outlined, little attention has been paid to the role of MD simulations for exploring its barrier function and synthesis. Herein, we give an overview about the impact of MD simulations on gaining insight into the shield role and synthesis pathway of LPS, which have attracted considerable attention to discover molecules able to surmount antibiotic resistance, either circumventing LPS defenses or disrupting its synthesis. We specifically focus on the enhanced sampling and free energy calculation methods that have been combined with MD simulations to address such research. We also highlight the use of special-purpose MD supercomputers, the importance of appropriate LPS and ions parameterization to obtain reliable results, and the complementary views that MD and wet-lab experiments provide. Thereby, this work, which covers the last five years of research, apart from outlining the phenomena and strategies that are being explored, evidences the valuable insights that are gained by MD, which may be useful to advance antibiotic design, and what the prospects of this in silico method could be in LPS research.

Peptide-Based Drug Delivery Systems

Peptide-based drug delivery systems have many advantages when compared to synthetic systems in that they have better biocompatibility, biochemical and biophysical properties, lack of toxicity, controlled molecular weight via solid phase synthesis and purification. Lysosomes, solid lipid nanoparticles, dendrimers, polymeric micelles can be applied by intravenous administration, however they are of artificial nature and thus may induce side effects and possess lack of ability to penetrate the blood-brain barrier. An analysis of nontoxic drug delivery systems and an establishment of prospective trends in the development of drug delivery systems was needed. This review paper summarizes data, mainly from the past 5 years, devoted to the use of peptide-based carriers for delivery of various toxic drugs, mostly anticancer or drugs with limiting bioavailability. Peptide-based drug delivery platforms are utilized as peptide–drug conjugates, injectable biodegradable particles and depots for delivering small molecule pharmaceutical substances (500 Da) and therapeutic proteins. Controlled drug delivery systems that can effectively deliver anticancer and peptide-based drugs leading to accelerated recovery without significant side effects are discussed. Moreover, cell penetrating peptides and their molecular mechanisms as targeting peptides, as well as stimuli responsive (enzyme-responsive and pH-responsive) peptides and peptide-based self-assembly scaffolds are also reviewed.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Multiple myeloma: therapeutic delivery of antibodies and aptamers

Multiple myeloma is the second most common hematological malignancy in adults, accounting for 2% of all cancer-related deaths in the UK. Current chemotherapy-based regimes are insufficient, as most patients relapse and develop therapy resistance. This review focuses on current novel antibody- and aptamer-based therapies aiming to overcome current therapy limitations, as well as their respective limitations and areas of improvement. The use of computer modeling methods, as a tool to study and improve ligand-receptor alignments for the use of novel therapy development will also be discussed, as it has become a rapid, reliable and comparatively inexpensive method of investigation.

Machine learning directed drug formulation development

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

Systematic Modification and Evaluation of Enzyme-Loaded Chitosan Nanoparticles

Polymeric-based nano drug delivery systems have been widely exploited to overcome protein instability during formulation. Presently, a diverse range of polymeric agents can be used, among which polysaccharides, such as chitosan (CS), hyaluronic acid (HA) and cyclodextrins (CDs), are included. Due to its unique biological and physicochemical properties, CS is one of the most used polysaccharides for development of protein delivery systems. However, CS has been described as potentially immunogenic. By envisaging a biosafe cytocompatible and haemocompatible profile, this paper reports the systematic development of a delivery system based on CS and derived with HA and CDs to nanoencapsulate the model human phenylalanine hydroxylase (hPAH) through ionotropic gelation with tripolyphosphate (TPP), while maintaining protein stability and enzyme activity. By merging the combined set of biopolymers, we were able to effectively entrap hPAH within CS nanoparticles with improvements in hPAH stability and the maintenance of functional activity, while simultaneously achieving strict control of the formulation process. Detailed characterization of the developed nanoparticulate systems showed that the lead formulations were internalized by hepatocytes (HepG2 cell line), did not reveal cell toxicity and presented a safe haemocompatible profile.

Universal description of steric hindrance in flexible polymer gels

In this work, the long-time diffusion of a solute in a chemically crosslinked and flexible hydrogel is computed from a bead-spring model of a polymeric network to assess the effect of steric obstruction. The relative diffusivities obtained for a wide variety of systems can be described by an exponential decay depending on a parameter that differs from that employed for rigid gels. The mathematical expression derived here can approximately predict the diffusivity in flexible gels if steric hindrance is the mechanism ruling diffusion.

Surface Functionalization of Graphene-Based Materials: Biological Behavior, Toxicology, and Safe-By-Design Aspects

The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.