Navigating Skin Delivery Horizon: An Innovative Approach in Pioneering Surface Modification of Ultradeformable Vesicles.

In the dynamic landscape of pharmaceutical advancements, the strategic application of active pharmaceutical ingredients to the skin through topical and transdermal routes has emerged as a compelling avenue for therapeutic interventions. This non-invasive approach has garnered considerable attention in recent decades, with numerous attempts yielding approaches and demonstrating substantial clinical potential. However, the formidable barrier function of the skin, mainly the confinement of drugs on the upper layers of the stratum corneum, poses a substantial hurdle, impeding successful drug delivery via this route. Ultradeformable vesicles/carriers (UDVs), positioned within the expansive realm of nanomedicine, have emerged as a promising tool for developing advanced dermal and transdermal therapies. The current review focuses on improving the passive dermal and transdermal targeting capacity by integrating functionalization groups by strategic surface modification of drug-loaded UDV nanocarriers. The present review discusses the details of case studies of different surface-modified UDVs with their bonding strategies and covers the recent patents and clinical trials. The design of surface modifications holds promise for overcoming existing challenges in drug delivery by marking a significant leap forward in the field of pharmaceutical sciences.

Accelerated Predictive Stability Study of a Pediatric Drug Product for a Supplemental New Drug Application.

In this paper, we report two Accelerated Stability Assessment Program (ASAP) studies for a pediatric drug product. Whereas the first study using a generic design failed to establish a predictive model, the second one was successful after troubleshooting the first study and customizing the study conditions. This work highlighted important lessons learned from designing an ASAP study for formulations containing excipients that could undergo phase change at high humidity levels. The stability predictions by the second ASAP model were consistent with available long-term stability data of the drug product under various storage conditions in two different packaging configurations. The ASAP model was part of the justifications accepted by the health authority to submit a stability package with reduced long-term stability data from the primary stability batches for a Supplemental New Drug Application (sNDA).

Advances in Nanotechnology for Enhancing the Solubility and Bioavailability of Poorly Soluble Drugs.

This manuscript offers a comprehensive overview of nanotechnology's impact on the solubility and bioavailability of poorly soluble drugs, with a focus on BCS Class II and IV drugs. We explore various nanoscale drug delivery systems (NDDSs), including lipid-based, polymer-based, nanoemulsions, nanogels, and inorganic carriers. These systems offer improved drug efficacy, targeting, and reduced side effects. Emphasizing the crucial role of nanoparticle size and surface modifications, the review discusses the advancements in NDDSs for enhanced therapeutic outcomes. Challenges such as production cost and safety are acknowledged, yet the potential of NDDSs in transforming drug delivery methods is highlighted. This contribution underscores the importance of nanotechnology in pharmaceutical engineering, suggesting it as a significant advancement for medical applications and patient care.

Selective detection of azelnidipine in pharmaceuticals via carbon dot mediated spectrofluorimetric method: A green approach.

A spectrofluorimetric method using fluorescent carbon dots (CDs) was developed for the selective detection of azelnidipine (AZEL) pharmaceutical in the presence of other drugs. In this study, N-doped CDs (N-CDs) were synthesized through a single-step hydrothermal process, using citric acid and urea as precursor materials. The prepared N-CDs showed a highly intense blue fluorescence emission at 447 nm, with a photoluminescence quantum yield of ~21.15% and a fluorescence lifetime of 0.47 ns. The N-CDs showed selective fluorescence quenching in the presence of all three antihypertensive drugs, which was used as a successful detection platform for the analysis of AZEL. The photophysical properties, UV-vis light absorbance, fluorescence emission, and lifetime measurements support the interaction between N-CDs and AZEL, leading to fluorescence quenching of N-CDs as a result of ground-state complex formation followed by a static fluorescence quenching phenomenon. The detection platform showed linearity in the range 10-200 µg/ml (R2 = 0.9837). The developed method was effectively utilized for the quantitative analysis of AZEL in commercially available pharmaceutical tablets, yielding results that closely align with those obtained from the standard method (UV spectroscopy). With a score of 0.76 on the 'Analytical GREEnness (AGREE)' scale, the developed analytical method, incorporating 12 distinct green analytical chemistry components, stands out as an important technique for estimating AZEL.

Quality by Design Perspective for Designing Foam-based Formulation: Current State of Art.

Foam-based delivery systems contain one or more active ingredients and dispersed solid or liquid components that transform into gaseous form when the valve is actuated. Foams are an attractive and effective delivery approach for medical, cosmetic, and pharmaceutical uses. The foams-based delivery systems are gaining attention due to ease of application as they allow direct application onto the affected area of skin without using any applicator or finger, hence increasing the compliance and satisfaction of the patients. In order to develop foam-based delivery systems with desired qualities, it is vital to understand which type of material and process parameters impact the quality features of foams and which methodologies may be utilized to investigate foams. For this purpose, Quality-by-Design (QbD) approach is used. It aids in achieving quality-based development during the development process by employing the QbD concept. The critical material attributes (CMAs) and critical process parameters (CPPs) were discovered through the first risk assessment to ensure the requisite critical quality attributes (CQAs). During the initial risk assessment, the high-risk CQAs were identified, which affect the foam characteristics. In this review, the authors discussed the various CMAs, CPPs, CQAs, and risk factors associated in order to develop an ideal foam-based formulation with desired characteristics.

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure-driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

Accelerating Process Development and Product Formulation.

Currently, the lengthy time needed to bring new drugs to market or to implement postapproval changes causes multiple problems, such as delaying patients access to new lifesaving or life-enhancing medications and slowing the response to emergencies that require new treatments. However, new technologies are available that can help solve these problems. The January 2023 NIPTE pathfinding workshop on accelerating drug product development and approval included a session in which participants considered the current state of product formulation and process development, barriers to acceleration of the development timeline, and opportunities for overcoming these barriers using new technologies. The authors participated in this workshop, and in this article have shared their perspective of some of the ways forward, including advanced manufacturing techniques and adaptive development. In addition, there is a need for paradigm shifts in regulatory processes, increased pre-competitive collaboration, and a shared strategy among regulators, industry, and academia.

Pharmaceutically active compounds removal from aqueous solutions by MIL-101(Cr)-NH2: A molecular dynamics study.

Discharging pharmaceutically active drugs into water and wastewater has become a significant environmental threat. Traditional methods are unable to effectively remove these compounds from wastewater, so it is necessary to search for more effective methods. This study investigates the potential of MIL-101(Cr)-NH2 as a preferable and more effective adsorbent for the adsorption and removal of pharmaceutically active compounds from aqueous solutions. By utilizing its large porosity, high specific surface area, and high stability, the structural and transport properties of three pharmaceutically active compounds naproxen (NAP), diclofenac (DIC) and sulfamethoxazole (SMX)) studied using molecular dynamics simulation. The results indicate that the MIL-101(Cr)-NH2 adsorbent is suitable for removing drug molecules from aqueous solutions, with maximum adsorption capacities of 697.75 mg/g for naproxen, 704.99 mg/g for diclofenac, and 725.51 mg/g for sulfamethoxazole.

The potential of three-dimensional printing for pediatric oral solid dosage forms.

Pediatric patients often require individualized dosing of medicine due to their unique pharmacokinetic and developmental characteristics. Current methods for tailoring the dose of pediatric medications, such as tablet splitting or compounding liquid formulations, have limitations in terms of dosing accuracy and palatability. This paper explores the potential of 3D printing as a solution to address the challenges and provide tailored doses of medication for each pediatric patient. The technological overview of 3D printing is discussed, highlighting various 3D printing technologies and their suitability for pharmaceutical applications. Several individualization options with the potential to improve adherence are discussed, such as individualized dosage, custom release kinetics, tablet shape, and palatability. To integrate the preparation of 3D printed medication at the point of care, a decentralized manufacturing model is proposed. In this setup, pharmaceutical companies would routinely provide materials and instructions for 3D printing, while specialized compounding centers or hospital pharmacies perform the printing of medication. In addition, clinical opportunities of 3D printing for dose-finding trials are emphasized. On the other hand, current challenges in adequate dosing, regulatory compliance, adherence to quality standards, and maintenance of intellectual property need to be addressed for 3D printing to close the gap in personalized oral medication.

Taste-masking methods in multiparticulate dosage forms with a focus on poorly soluble drugs.

In the past, the administration of medicines for children mainly involved changes to adult dosage forms, such as crushing tablets or opening capsules. However, these methods often led to inconsistent dosing, resulting in under- or overdosing. To address this problem and promote adherence, numerous initiatives, and regulatory frameworks have been developed to develop more child-friendly dosage forms. In recent years, multiparticulate dosage forms such as mini-tablets, pellets, and granules have gained popularity. However, a major challenge that persists is effectively masking the bitter taste of drugs in such formulations. This review therefore provides a brief overview of the current state of the art in taste masking techniques, with a particular focus on taste masking by film coating. Methods for evaluating the effectiveness of taste masking are also discussed and commented on. Another important issue that arises frequently in this area is achieving sufficient dissolution of poorly water-soluble drugs. Since the simultaneous combination of sufficient dissolution and taste masking is particularly challenging, the second objective of this review is to provide a critical summary of studies dealing with multiparticulate formulations that are tackling both of these issues.

Lipid-based systems with precipitation inhibitors as formulation approach to improve the drug bioavailability and/or lower its dose: a review.

Lipid-based systems, such as self-microemulsifying systems (SMEDDS) are attracting strong attention as a formulation approach to improve the bioavailability of poorly water-soluble drugs. By applying the "spring and parachute" strategy in designing supersaturable SMEDDS, it is possible to maintain the drug in the supersaturated state long enough to allow absorption of the complete dose, thus improving the drug's bio-availability. As such an approach allows the incorporation of larger amounts of the drug in equal or even lower volumes of SMEDDS, it also enables the production of smaller final dosage forms as well as decreased gastrointestinal irritation, being of particular importance when formulating dosage forms for children or the elderly. In this review, the technological approaches used to prolong the drug supersaturation are discussed regarding the type and concentration of polymers used in liquid and solid SMEDDS formulation. The addition of hypromellose derivatives, vinyl polymers, polyethylene glycol, polyoxyethylene, or polymetacrylate copolymers proved to be effective in inhibiting drug precipitation. Regarding the available literature, hypromellose has been the most commonly used polymeric precipitation inhibitor, added in a concentration of 5 % (m/m). However, the inhibiting ability is mainly governed not only by the physicochemical properties of the polymer but also by the API, therefore the choice of optimal precipitation inhibitor is recommended to be evaluated on an individual basis.

Degradation of pharmaceuticals and other emerging pollutants employing bi-metal catalysts/magnesium and/or (green) hydrogen in aqueous solution.

Contaminations by pharmaceuticals, personal care products, and other emerging pollutants in water resources have become a seriously burgeoning issue of global concern in the first third of the twenty-first century. As societal reliance on pharmaceuticals continues to escalate, the inadvertent introduction of these substances into water reservoirs poses a consequential environmental threat. Therefore, the aim of this study was to investigate reductive degradation, particularly, catalytic hydrogenation regarding model pollutants such as diclofenac (DCF), ibuprofen (IBP), 17α-ethinylestradiol (EE2), or bisphenol-A (BPA), respectively,  in aqueous solutions at lab scale. Iron bimetals (zero valent iron, ZVI, and copper, Cu, or nickel, Ni) as well as zero valent magnesium (Mg, ZVM) in combination with  rhodium, Rh, or palladium, Pd, as hydrogenation catalysts (HK), were investigated. Studies were executed through various short-term batch experiments, with multiple sample collections, over a total range of 120 min. The results indicated that DCF was attenuated at over 90 % when exposed to Fe-Cu or a Fe-Ni bimetal (applied as a single model pollutant). However, when DCF was part of a mixture alongside with IBP, EE2, and BPA, the attenuation efficacy decreased to 79 % with Fe-Cu and 23 % with Fe-Ni. Conversely, both IBP and BPA exhibit notably low attenuation levels with both bimetals, less than 50 %, both deployed as single substances or in mixtures. No reaction (degradation) products could be identified employing LC-MS, but sometimes a release of the parent pollutant when applying an acetic acid buffer could be noted to a certain extent, suggesting adsorption processes on corrosion products such as iron hydroxide and/or oxides. Surprisingly, Mg in combination with Rh (Rh-HK) or Pd (Pd-HK) showed a significantly rapid decrease in the concentrations of DCF, EE2, and BPA, in part up to approximately 100 %, that is, within a few minutes only in part due to hydrogenation degradation reactions (related reaction products could actually be identified by LC-MS; adsorption processes were not observed here). Moreover, kinetic modeling of the DCF degradation with Mg-Rh-HK was conducted at different temperatures (15 °C, 20 °C, 25 °C, 35 °C) and varied initial concentrations (2.5 mg/L, 5.0 mg/L, 7.5 mg/L, 10.0 mg/L). The outcomes prove that the degradation of DCF at the Rh-HK's surface followed a modified first-order kinetics, most probably by catalytic hydrodehalogenation and subsequent hydrogenation of the aromatic moieties (molecular hydrogen was provided by the corrosion of Mg). From the determined reaction rate constants at four different temperatures, the activation energy was estimated to be 59.6 kJ/mol by means of the Arrhenius equation what is in good agreement with similar results reported in the literature. This coupled hydrodehalogenation and hydrogenation approach may be upscaled into a new promising technical process for comprehensively removing such pharmaceuticals and similar pollutants in sewage plants in a single step, furthermore, even in combination with adsorption by activated carbon and/or ozonation which have already been established at some sewage plants in Switzerland and Germany recently.

Prediction of Drug-Target Affinity Using Attention Neural Network.

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

Predictive computational models for assessing the impact of co-milling on drug dissolution.

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

Emerging opportunities of using large language models for translation between drug molecules and indications.

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.

INTRODUCTION: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (koff and kon) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

The VERA software: Implementation of the acute fish toxicity endpoint and its application to pharmaceutical compounds.

The Virtual Extensive Read-Across software (VERA) is a new tool for read-across using a global similarity score, molecular groups, and structural alerts to find clusters of similar substances; these clusters are then used to identify suitable similar substances and make an assessment for the target substance. A beta version of VERA GUI is free and available at vegahub.eu; the source code of the VERA algorithm is available on GitHub. In the past we described its use to assess carcinogenicity, a classification endpoint. The aim here is to extend the automated read-across approach to assess continuous endpoints as well. We addressed acute fish toxicity. VERA evaluation on the acute fish toxicity endpoint was done on a dataset containing general substances (pesticides, industrial products, biocides, etc.), obtaining an overall R2 of 0.68. We employed the VERA algorithm also on active pharmaceutical ingredients (APIs). We included a portion of the APIs in the training dataset to predict APIs, successfully achieving an overall R2 of 0.63. VERA evaluates the assessment's reliability, and we reached an R2 of 0.78 and Root Mean Square Error (RMSE) of 0.44 for predictions with high reliability.

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the ε hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

Rapid screening of drugs in environmental water using metal organic framework/Ti3C2Tx composite coated blade spray-mass spectrometry.

Simultaneous rapid screening of multiple drugs of abuse in environmental water facilitates effective monitoring and trend assessments. Herein, a novel porphyrin-based metal organic frameworks modified Ti3C2Tx nanosheets (Cu-TCPP/Ti3C2Tx) composite was prepared and utilized as solid-phase microextraction (SPME) coating for the simultaneous analysis of 21 drugs from water samples. The composite was embedded with matrix-compatible polyacrylonitrile binder to prepare a coated blade with thin and uniform coating layer. Ambient mass spectrometry (MS) technique was used to create a coated blade spray-MS (CBS-MS) method for the quantitative determination of drugs in water samples. High throughput and automated sample preparation were achieved with the use of a Concept 96-well plate system, enabling analysis of 21 drugs of abuse within 1 min per sample, while using only 8 µL of organic solvent for desorption and CBS-MS detection. The developed method showed favorable linearity (R2 ≥ 0.9983) in the range of 0.05 to 10 ng mL-1, low limits of detection (1.5-9.0 ng L-1), sufficient recovery (67.6-133.2%), as well as satisfactory precision (RSDs≤13.5%). This study not only delivers a novel and efficient SPME coating composite, but also demonstrates the excellent performance of a high-throughput, efficient, and green analytical method for determination of drugs in environmental water.