Computational modeling for formulation design

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

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

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

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

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

Drug solubilization in dog intestinal fluids with and without administration of lipid-based formulations

The use of animal experiments can be minimized with computational models capable of reflecting the simulated environments. One such environment is intestinal fluid and the colloids formed in it. In this study we used molecular dynamics simulations to investigate solubilization patterns for three model drugs (carvedilol, felodipine and probucol) in dog intestinal fluid, a lipid-based formulation, and a mixture of both. We observed morphological transformations that lipids undergo due to the digestion process in the intestinal environment. Further, we evaluated the effect of bile salt concentration and observed the importance of interindividual variability. We applied two methods of estimating solubility enhancement based on the simulated data, of which one was in good qualitative agreement with the experimentally observed solubility enhancement. In addition to the computational simulations, we also measured solubility in i) aspirated dog intestinal fluid samples and ii) simulated canine intestinal fluid in the fasted state, and found there was no statistical difference between the two. Hence, a simplified dissolution medium suitable for in vitro studies provided physiologically relevant data for the systems explored. The computational protocol used in this study, coupled with in vitro studies using simulated intestinal fluids, can serve as a useful prescreening tool in the process of drug delivery strategies development.

Insights into Hildebrand Solubility Parameters - Contributions from Cohesive Energies or Electrophilicity Densities?

We introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non- or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Utilizing Molecular Simulations to Examine Nanosuspension Stability

Drug nanosuspensions offer a promising approach to improve bioavailability for poorly soluble drug candidates. Such formulations often necessitate the inclusion of an excipient to stabilize the drug nanoparticles. However, the rationale for the choice of the correct excipient for a given drug candidate remains unclear. To gain molecular insight into formulation design, this work first utilizes a molecular dynamics simulation to computationally investigate drug-excipient interactions for a number of combinations that have been previously studied experimentally. We find that hydrophobic interactions drive excipient adsorption to drug nanoparticles and that the fraction of polar surface area serves as a predictor for experimental measurements of nanosuspension stability. To test these ideas prospectively, we applied our model to an uncharacterized drug compound, GDC-0810. Our simulations predicted that a salt form of GDC-0810 would lead to more stable nanosuspensions than the neutral form; therefore, we tested the stability of salt GDC-0810 nanosuspensions and found that the salt form readily formed nanosuspensions even without the excipient. To avoid computationally expensive simulations in the future, we extended our model by showing that simple, two-dimensional properties of single drug molecules can be used to rationalize nanosuspension designs without simulations. In all, our work demonstrates how computational tools can provide molecular insight into drug-excipient interactions and aid in rational formulation design.

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

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

Multiphysics Simulation of Local Transport and Absorption Coupled with Pharmacokinetic Modeling of Systemic Exposure of Subcutaneously Injected Drug Solution

INTRODUCTION

Subcutaneous (SC) injectables have become more acceptable and feasible for administration of biologics and small molecules. However, efficient development of these products is limited to costly and time-consuming techniques, partially because absorption mechanisms and kinetics at the local site of injection remain poorly understood.

OBJECTIVE

To bridge formulation critical quality attributes (CQA) of injectables with local physiological conditions to predict systemic exposure of these products.

METHODOLOGY

We have previously developed a multiscale, multiphysics computational model to simulate lymphatic absorption and whole-body pharmacokinetics of monoclonal antibodies. The same simulation framework was applied in this study to compute the capillary absorption of solubilized small molecule drugs that are injected subcutaneously. Sensitivity analyses were conducted to probe the impact by key simulation parameters on the local and systemic exposures.

RESULTS

This framework was capable of determining which parameters had the biggest impact on small molecule absorption in the SC. Particularly, membrane permeability of a drug was found to have the biggest impact on drug absorption kinetics, followed by capillary density and drug diffusivity.

CONCLUSION

Our modelling framework proved feasible in predicting local transport and systemic absorption from the injection site of small molecules. Understanding the effect of these properties and how to model them may help to greatly expedite the development process.

Liposomes characterization for market approval as pharmaceutical products: Analytical methods, guidelines and standardized protocols

Liposomes are nano-sized lipid-based vesicles widely studied for their drug delivery capabilities. Compared to standard carries they exhibit better properties such as improved site-targeting and drug release, protection of drugs from degradation and clearance, and lower toxic side effects. At present, scientific literature is rich of studies regarding liposomes-based systems, while 14 types of liposomal products have been authorized to the market by EMA and FDA and many others have been approved by national agencies. Although the interest in nanodevices and nanomedicine has steadily increased in the last two decades the development of documentation regulating and standardizing all the phases of their development and quality control still suffers from major inadequacy due to the intrinsic complexity of nano-systems characterization. Many generic documents (Type 1) discussing guidelines for the study of nano-systems (lipidic and not) have been proposed while there is a lack of robust and standardized methods (Type 2 documents). As a result, a widespread of different techniques, approaches and methodologies are being used, generating results of variable quality and hard to compare with each other. Additionally, such documents are often subject to updates and rewriting further complicating the topic. Within this context the aim of this work is focused on bridging the gap in liposome characterization: the most recent standardized methodologies suitable for liposomes characterization are here reported (with the corresponding Type 2 documents) and revised in a short and pragmatical way focused on providing the reader with a practical background of the state of the art. In particular, this paper will put the accent on the methodologies developed to evaluate the main critical quality attributes (CQAs) necessary for liposomes market approval.

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

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

Hot-melt extruded in situ gelling systems (MeltDrops Technology): Formulation development, in silico modelling and in vivo studies

In situ gelling systems (ISGS) can prolong retention time and bioavailability of ophthalmic solutions. The complexity and cost of ISGS avert their industrial scale-up and clinical implementation. In this study, we demonstrate novel application of hot-melt extrusion (HME) technology for continuous manufacturing of ISGS (MeltDrops Technology). Timolol maleate (TIM) and dorzolamide hydrochloride (DRZ) loaded MeltDrops were successfully developed using HME for glaucoma management, thereby resolving issues with batch manufacturing of ISGS, prolonging retention time thus improving bioavailability. The MeltDrops technology involves one-step, i.e., passing all the ingredients through an extruder at a screw speed between 20-50 rpm and barrel temperature of 80 °C. The comparative evaluation of MeltDrops and batch-processed ISGS demonstrated that MeltDrops exhibited better physical and chemical content uniformity. The extrusion temperature and screw speed were critical factors influencing content uniformity and properties of the MeltDrops. MeltDrops showed sustained drug release for >12 hours in vitro (TIM= 83.07%; DRZ = 60.43%, 12hours) versus marketed eyedrops. The developed MeltDrops followed Peppas-Sahlin model, combining Fickian diffusion and swelling processes. The in vivo study in New Zealand rabbits revealed superior effectiveness and safety of the MeltDrops as compared to the marketed eyedrops. Herein we conclude, MeltDrops would serve as a cutting-edge platform technology that can be used to manufacture various ISGS with one-step processability, cost-effectiveness, and improved product quality, which are otherwise processed by batch manufacturing that involves numerous complex processing steps.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Interaction of Phospholipid, Cholesterol, Beta-Carotene, and Vitamin C Molecules in Liposome-Based Drug Delivery Systems: An In Silico Study

This paper investigates the interaction within a liposome-based drug delivery system in silico. Results confirmed that phospholipids, cholesterol, beta-carotene, and vitamin C in the liposome structures interact noncovalently. The formation of noncovalent interactions indicates that the liposomal structures from phospholipid molecules will not result in chemical changes to the drug or any molecules encapsulated within. Noncovalent interactions formed include (i) moderate-strength hydrogen bonds with interaction energies ranging from -73.6434 kJ·mol^-1 to -45.6734 kJ·mol^-1 and bond lengths ranging from 1.731 Å to 1.827 Å and (ii) van der Waals interactions (induced dipole-induced dipole and induced dipole-dipole interactions) with interaction energies ranging from -4.4735 kJ·mol^-1 to -1.5840 kJ·mol^-1 and bond lengths ranging from 3.192 Å to 3.742 Å. The studies for several phospholipids with short hydrocarbon chains show that changes in chain length have almost no effect on interaction energy, bond length, and partial atomic charge.

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Biomechanical evaluation on a novel design of biodegradable embossed locking compression plate for orthopaedic applications using finite element analysis

In orthopaedics, conventional implant plates such as locking compression plate (LCP) made from non-biodegradable materials play a vital role in the fixation to support bone fractures, but also create a complication such as stress shielding. These again require a painful surgery to remove/replace after they have healed as it does not degrade into the physiological environment (PE). Currently, there has already been enough discovery of biodegradable materials that, despite being mechanically inefficient compared to non-biodegradable materials, can completely be biodegraded in PE during and after healing to avoid such problems. While there has been insufficient research on the design of biodegradable implant plates, the implementation of which may help achieve the goal with an effort of high mechanical strength. A novel design of biodegradable embossed locking compression plate (BELCP) is designed for biodegradable materials to approach superior mechanical performance and complete degradation over time, considering all such parameters and factors. For biomechanical evaluation, four-point bending test (4PBT), axial compressive and tensile test (ACTT) and torsion test (TT) have been performed on LCP, BELCP and its continuously degraded forms made of biodegradable material (Mg-alloy) using finite element method. BELCP has found 50%, 100% and 100% higher mechanical performance and safer in 4PBT, ACTT and TT, respectively, than LCP. Moreover, BELCP has also observed safe during continuous degradation up to 6 months after implantation under these three tests, considering an approximate sustained degradation rate of about 4 mm/year. Even Mg-alloy made BELCP can be sufficient and safer to support fractured bone than SS-alloy made LCP, but not Ti-alloy made LCP. BELCP can be a successful biodegradable bone implant plate after human/animal trials in the future.

Nano-hydroxyapatite improves intestinal absorption of acetazolamide (BCS Class IV drug)–but how?

We earlier reported that coating poorly water-soluble drugs with nano-hydroxyapatite (nano-HAP) improves bioavailability after oral administration. In the present study, we coated BCS Class IV drug acetazolamide (AZ) with nano-HAP (AZ/HAP formulation), and investigated its bioavailability and nano-HAP’s role in promoting it. We tested AZ bioavailability after a single oral dose of the AZ/HAP formulation in rats, followed by a series of in vitro, ex vivo and in vivo testing. The binding state of AZ and nano-HAP was analyzed by gel filtration chromatography. AZ permeability was studied using a Caco-2 cell monolayer assay kit, to test for tight junction penetration, then using an Ussing chamber mounted with intestinal epithelium, both with and without Peyer’s patch tissue, to examine the role of intracellular transport. Fluorescence-labeled nano-HAP particles were administered orally in rats to investigate their localization in the intestinal tract. The area under the blood concentration time-curve in rats was about 4 times higher in the AZ/HAP formulation group than in the untreated AZ group. Gel filtration analysis showed AZ and nano-HAP were not bound. The Caco-2 study showed equivalent AZ permeability for both groups, but without significant change in transepithelial electrical resistance (TEER), indicating that tight junctions were not penetrated. In the Ussing chamber study, no significant difference in AZ permeability between the two groups was observed for epithelium containing Peyer’s patch tissue, but for epithelium without Peyer’s patch tissue, at high concentration, significantly higher permeability in the AZ/HAP formulation group was observed. Fluorescent labeling showed nano-HAP particles were present in both intestinal villi and Peyer’s patch tissue 30 min after oral administration. Our results suggest that nano-HAP’s enhancement of drug permeability from the small intestine occurs not via tight junctions, but intracellularly, via the intestinal villi. Further study to elucidate the mechanism of this permeability enhancement is required.

Drug-dendrimer complexes and conjugates: Detailed furtherance through theory and experiments

Dendritic nanovectors-based drug delivery has gained significant attention in the past couple of decades. Dendrimers play a crucial role in deciding the solubility of sparingly soluble drug molecules and help in improving pharmacokinetics. A few important steps in drug delivery through dendrimers, such as drug encapsulation, formulation, and target-specific delivery, play an important role in deciding the fate of a drug molecule. It is also of prime importance to understand the interactions between a drug molecule and dendrimers at atomistic levels to decode the mechanism of action of drug-dendrimer complexes and their reliability in terms of drug delivery. Colossal progress in current experimental and computational approaches in the field has resulted in a vast amount of data that needs to be curated to be further implemented efficiently. Improved computational power has led to greater accuracy and prompt predictions of properties of drug-dendrimer complexes and their mechanism of action. The current review encapsulates the pioneering work in the field, experimental achievements in terms of drug delivery, and newer computational techniques employed in the advancement of the field.

An in Silico Approach to Reveal the Nanodisc Formulation of Doxorubicin

Molecular dynamic behaviors of nanodisc (ND) formulations of free doxorubicin (DOX) and DOX conjugated lipid prodrug molecules were investigated by molecular dynamics (MD) simulations. We have unveiled how formulation design affects the drug release profile and conformational stability of ND assemblies. Our simulation results indicate that free DOX molecules loaded in the ND system experienced rapid dissociation due to the unfavorable orientation of DOX attached to the lipid surface. It is found that DOX tends to form aggregates with higher drug quantities. In contrast, lipidated DOX-prodrugs incorporated in ND formulations exhibited sufficient ND conformational stability. The drug loading capacity is dependent on the type of lipid molecules grafted on the DOX-prodrug, and the drug loading quantities in a fixed area of NDs follow the order: DOX-BMPH-MP > DOX-BMPH-TC > DOX-BMPH-PTE. To gain further insight into the dynamic characteristics of ND formulations governed by different kinds of lipidation, we investigated the conformational variation of ND components, intermolecular interactions, the solvent accessible surface area, and individual MSP1 residue flexibility. We found that the global conformational stability of DOX-prodrug-loaded ND assemblies is influenced by the molecular flexibility and lipidated forms of DOX-prodrug. We also found that the spontaneous self-aggregation of DOX-prodrugs with increasing quantities on ND could reduce the membrane fluidity and enhance the conformational stability of ND formulations.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Computational Modeling of Drug Dissolution in the Human Stomach

A computational model of drug dissolution in the human stomach is developed to investigate the interaction between gastric flow and orally administrated drug in the form of a solid tablet. The stomach model is derived from the anatomical imaging data and the motion and dissolution of the drug in the stomach are modeled via fluid-structure interaction combined with mass transport simulations. The effects of gastric motility and the associated fluid dynamics on the dissolution characteristics are investigated. Two different pill densities are considered to study the effects of the gastric flow as well as the gravitational force on the motion of the pill. The average mass transfer coefficient and the spatial distributions of the dissolved drug concentration are analyzed in detail. The results show that the retropulsive jet and recirculating flow in the antrum generated by the antral contraction wave play an important role in the motion of the pill as well as the transport and mixing of the dissolved drug concentration. It is also found that the gastric flow can increase the dissolution mass flux, especially when there is substantial relative motion between the gastric flow and the pill.

Scrutinizing the Feasibility of Nonionic Surfactants to Form Isotropic Bicelles of Curcumin: a Potential Antiviral Candidate Against COVID-19

Investigating bicelles as an oral drug delivery system and exploiting their structural benefits can pave the way to formulate hydrophobic drugs and potentiate their activity. Herein, the ability of non-ionic surfactants (labrasol®, tween 80, cremophore EL and pluronic F127) to form curcumin loaded bicelles with phosphatidylcholine, utilizing a simple method, was investigated. Molecular docking was used to understand the mechanism of bicelles formation. The % transmittance and TEM exhibited bicelles formation with labrasol® and tween 80, while cremophor EL and pluronic F127 tended to form mixed micelles. The surfactant-based nanostructures significantly improved curcumin dissolution (99.2 ± 2.6% within 10 min in case of tween 80-based bicelles) compared to liposomes and curcumin suspension in non-sink conditions. The prepared formulations improved curcumin ex vivo permeation over liposomes and drug suspension. Further, the therapeutic antiviral activity of the formulated curcumin against SARS-CoV-2 was potentiated over drug suspension. Although both Labrasol® and tween 80 bicelles could form bicelles and enhance the oral delivery of curcumin when compared to liposomes and drug suspension, the mixed micelles formulations depicted superiority than bicelles formulations. Our findings provide promising formulations that can be utilized for further preclinical and clinical studies of curcumin as an antiviral therapy for COVID-19 patients. Graphical Abstract.

Effectiveness of laddered embossed structure in a locking compression plate for biodegradable orthopaedic implants

Locking compression plate (LCP) has conventionally been the most extensively employed plate in internal fixation bone implants used in orthopaedic applications. LCP is usually made up of non-biodegradable materials that have a higher mechanical capability. Biodegradable materials, by and large, have less mechanical strength at the point of implantation and lose strength even more after a few months of continuous degradation in the physiological environment. To attain the adequate mechanical capability of a biodegradable bone implant plate, LCP has been modified by adding laddered - type semicircular filleted embossed structure. This improved design may be named as laddered embossed locking compression plate (LELCP). It is likely to provide additional mechanical strength with the most eligible biodegradable material, namely, Mg-alloy, even after continuous degradation that results in diminished thickness. For mechanical validation and comparison of LELCP made up of Mg-alloy, four-point bending test (4PBT) and axial compressive test (ACT) have been performed on LELCP, LCP and continuously degraded LELCP (CD-LELCP) with the aid of finite element method (FEM) for the assembly of bone segments, plate and screw segments. LELCP, when subjected to the above mentioned two tests, has been observed to provide 26% and 10.4% lower equivalent stress, respectively, than LCP without degradation. It is also observed mechanically safe and capable of up to 2 and 6 months of continuous degradation (uniform reduction in thickness) for 4PBT and ACT, respectively. These results have also been found reasonably accurate through real-time surgical simulations by approaching the most optimal mesh. According to these improved mechanical performance parameters, LELCP may be used or considered as a viable biodegradable implant plate option in the future after real life or in vivo validation.

Computational and Experimental Models of Type III Lipid-Based Formulations of Loratadine Containing Complex Nonionic Surfactants

Type III lipid-based formulations (LBFs) combine poorly water-soluble drugs with oils, surfactants, and cosolvents to deliver the drugs into the systemic circulation. However, the solubility of the drug can be influenced by the colloidal phases formed in the gastrointestinal tract as the formulation is dispersed and makes contact with bile and other materials present within the GI tract. Thus, an understanding of the phase behavior of LBFs in the gut is critical for designing efficient LBFs. Molecular dynamics (MD) simulation is a powerful tool for the study of colloidal systems. In this study, we modeled the internal structures of five type III LBFs of loratadine containing poly(ethylene oxide) nonionic surfactants polysorbate 80 and polyoxyl hydrogenated castor oil (Kolliphor RH40) using long-timescale MD simulations (0.4-1.7 μs). We also conducted experimental investigations (dilution of formulations with water) including commercial Claritin liquid softgel capsules. The simulations show that LBFs form continuous phase, water-swollen reverse micelles, and bicontinuous and phase-separated systems at different dilutions, which correlate with the experimental observations. This study supports the use of MD simulation as a predictive tool to determine the fate of LBFs composed of medium-chain lipids, polyethylene oxide surfactants, and polymers.

Drug Design: Where We Are and Future Prospects

Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.

Longitudinally centered embossed structure in the locking compression plate for biodegradable bone implant plate: a finite element analysis

In the current revolution of internal fixation implant in orthopaedics, a biodegradable implant is the most awaited and exceptional medical device where biodegradable material has paid more attention to the success of a biodegradable implant than the design of a biodegradable bone implant plate. By far, LCP is the most traditionally used implant plate (using non-biodegradable material) because of its experimental success, but not with qualified biodegradable material (Mg-alloy). This lack of mechanical performance is a major drawback that can be rectified by better structural design. This will help avoid few other problems as well. Therefore, with proper consideration, the LCP has been added to a semicircular filleted longitudinally centered embossed (LCE) structure to enhance overall mechanical performance that can help emphasize mechanical support even after continuous degradation when applied in a physiological environment. For mechanical verification of this advanced design of biodegradable bone implant plate, four-point bending test (4PBT) and axial compression test (ACT) have been performed using FEM on LCELCP, LCP, continuously degraded (CD)-LCELCP, and CD-LCP. LCELCP showed reduced stress of about 22% and 10% in 4PBT and ACT, respectively, compared to LCP. CD-LCELCP is safe during ACT over 6 months of continuous degradation when the degradation rate is assumed to be 4 mm/year. These results also ensured accuracy using mesh convergence and also mesh checked for quality assurance. Overall, LCELCP can be considered as a biodegradable bone implant plate because of its superior performance, if its ultimate validation is carried out through animal/human trials as future work.

Design approaches and challenges for biodegradable bone implants: a review

Introduction: Biodegradable materials have been at the forefront of cutting-edge research and offer a truly viable option in the designing and manufacturing of bone implants in biomedical engineering. Most research regarding these materials has focused on their biological characteristics and mechanical behavior vis-à-vis nonbiodegradable (NB) materials; but the design aspects and parametric configurations of biodegradable bone implant have somehow not received as much attention as they deserved.Area covered: This review aims to develop insight into the parametrically conceptualized design of biodegradable bone implant and takes into due consideration the characteristics of bone-biodegradable implant interface (BBII), design techniques employed for conventionally used bone implants to optimize parameters using standard test methods, traditional design, and finite element analysis approaches for implant and healing behavior, manufacturing techniques, real-time surgical simulations, and so on.Expert opinion: Some successful and conventionally used NB bone implants do not dissolve or degrade with time and require removal through a complicated surgery after fulfilling the intended objectives. These bone implants should be reconceptualized and designed with an appropriate biodegradable material while paying due attention to all factors/parameters involved and striking a balance between these factors with the ultimate objective of fulfilling all desired orthopedic requirements.

Design and analysis of biodegradable buttress threaded screws for fracture fixation in orthopedics: a finite element analysis

Screws made up of non-biodegradable materials (Ti-alloy, etc.) have been used since long for temporary joining/fixation in applications involving skeleton damage or bone fracture. These screws need to be removed after complete healing as their sustained presence results in many complications, such as - micro-fracturing, stress shielding, etc. The removal of these screws is a little difficult too as it may result in the healed bone getting broken/damaged again. These problems can be overcome by employing metallic implants (plate, screws, etc.) made up of biodegradable metallic materials (Mg-alloy, etc.). Such implants exhibit optimal mechanical performance, are biocompatible, have adequate biodegradation rates, and rely on a unique design. Internal fracture fixation makes usage of screws with or without an accompanying plate. Buttress-threaded screws are the most frequently used ones. These screws must have the capacity to bear usually occurring loads and hold fractured segments of bone all through the process of healing. Finite element analysis (FEA) is an effective technique used for testing and validation of desired characteristics for Mg-based biodegradable buttress-threaded screw (BBTS). The characteristics of interest include maximum possible pullout resistance to tightly hold segments of bone, torsional ability for tightening or tapping, bending ability during providing plate support by screw head, and resistance to combined loading (tensile/compressive and bending) during the self-support stage using merely the screw(s). According to test results and subsequent validation through discretization error and convergence plot, BBTS made up of Mg-alloy are found safe for regular applications under usually encountered impact loads. Topological optimization and vibration analysis are also performed wherein it is observed that design of BBTS is good enough for possible usage in fracture fixation in orthopaedics.

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.

Artificial intelligence in drug discovery and development

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SR_MC (r² = 0.81) and SR_LC (r² = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SR_MC r² = 0.74; SR_LC r² = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD_6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs.

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.

Multiple approaches for achieving drug solubility: an in silico perspective

Discovering new therapeutically active molecules is the ultimate destination in pharmaceutical research and development. Most drugs discovered are lipophilic and, hence, exhibit poor aqueous solubility, resulting in low bioavailability. Thus, there is a need to use various solubility enhancement techniques. Computational approaches enable the analysis of drug-carrier interactions or the numerous conformational changes in the carrier matrix that might establish an appropriate balance between cohesive and adhesive stability in a formulation. In this review, we discuss research approaches that provided molecular insight into drugs and their modifiers to unravel their solubility, stability, and bioavailability.

Simulation of Unit Operations in Formulation Development of Tablets Using Computational Fluid Dynamics

Tablets are the most customarily used solid oral unit dosage form for its better patient compliance. Preparation of these tablets include granulation, granule drying, die filling, and tablet coating as few unit operations and evaluation tests like dissolution test and disintegration test. These are the most crucial segments influencing the quality of the tablet. Critical analysis of the impact of factors like flow pattern, temperature, velocity, and other properties of fluid affecting the unit operations is obligatory to enhance their efficiency. Computational fluid dynamics (CFD), a combined mathematical and numerical approach, is used to analyze the process parameters of fluid affecting the abovementioned processes during tablet formulation. The equations governing the laws of conservation of energy, mass, and momentum are solved numerically utilizing CFD software for better understanding of the role of fluids within the tablet processing steps. This review not only focuses on discrete explanations on how CFD is utilized in formulation and evaluation of tablet but it is also a compilation of multiple research works performed on each unit operation by applying CFD.

Recent Advancements in Non-Invasive Formulations for Protein Drug Delivery

Advancements in biotechnology and protein engineering expand the availability of various therapeutic proteins including vaccines, antibodies, hormones, and growth factors. In addition, protein drugs hold many therapeutic advantages over small synthetic drugs in terms of high specificity and activity. This has led to further R&D investment in protein-based drug products and an increased number of drug approvals for therapeutic proteins. However, there are many biological and biopharmaceutical obstacles inherent to protein drugs including physicochemical and enzymatic destabilization, which limit their development and clinical application. Therefore, effective formulations of therapeutic proteins are needed to overcome the various physicochemical and biological barriers. In current medical practice, protein drugs are predominantly available in injectable formulations, which have disadvantages including pain, the possibility of infection, high cost, and low patient compliance. Consequently, non-invasive drug delivery systems for therapeutic proteins have gained great attention in the research and development of biomedicines. Therefore, this review covers the various formulation approaches to optimizing the delivery properties of protein drugs with an emphasis on improving bioavailability and patient compliance. It provides a comprehensive update on recent advancements in nanotechnologies with regard to non-invasive protein drug delivery systems, which is also categorized by the route of administrations including oral, nasal, transdermal, pulmonary, ocular, and rectal delivery systems.