Re-discover the value of protein binding assessments in hepatic and renal impairment studies and its contributions in drug labels and dose decisions.

One of the key pharmacokinetic properties of most small molecule drugs is their ability to bind to serum proteins. Unbound or free drug is responsible for pharmacological activity while the balance between free and bound drug can impact drug distribution, elimination, and other safety parameters. In the hepatic impairment (HI) and renal impairment (RI) clinical studies, unbound drug concentration is often assessed; however, the relevance and impact of the protein binding (PB) results is largely limited. We analyzed published clinical safety and pharmacokinetic studies in subjects with HI or RI with PB assessment up to October 2022 and summarized the contribution of PB results on their label dose recommendations. Among drugs with HI publication, 32% (17/53) associated product labels include PB results in HI section. Of these, the majority (9/17, 53%) recommend dose adjustments consistent with observed PB change. Among drugs with RI publication, 27% (12/44) of associated product labels include PB results in RI section with the majority (7/12, 58%) recommending no dose adjustment, consistent with the reported absence of PB change. PB results were found to be consistent with a tailored dose recommendation in 53% and 58% of the approved labels for HI and RI section, respectively. We further discussed the interpretation challenges of PB results, explored treatment decision factors including total drug concentration, exposure-response relationships, and safety considerations in these case examples. Collectively, comprehending the alterations in free drug levels in HI and RI informs treatment decision through a risk-based approach.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.

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.

Insights into Inhalation Drug Disposition: The Roles of Pulmonary Drug-Metabolizing Enzymes and Transporters.

The past five decades have witnessed remarkable advancements in the field of inhaled medicines targeting the lungs for respiratory disease treatment. As a non-invasive drug delivery route, inhalation therapy offers numerous benefits to respiratory patients, including rapid and targeted exposure at specific sites, quick onset of action, bypassing first-pass metabolism, and beyond. Understanding the characteristics of pulmonary drug transporters and metabolizing enzymes is crucial for comprehending efficient drug exposure and clearance processes within the lungs. These processes are intricately linked to both local and systemic pharmacokinetics and pharmacodynamics of drugs. This review aims to provide a comprehensive overview of the literature on lung transporters and metabolizing enzymes while exploring their roles in exogenous and endogenous substance disposition. Additionally, we identify and discuss the principal challenges in this area of research, providing a foundation for future investigations aimed at optimizing inhaled drug administration. Moving forward, it is imperative that future research endeavors to focus on refining and validating in vitro and ex vivo models to more accurately mimic the human respiratory system. Such advancements will enhance our understanding of drug processing in different pathological states and facilitate the discovery of novel approaches for investigating lung-specific drug transporters and metabolizing enzymes. This deeper insight will be crucial in developing more effective and targeted therapies for respiratory diseases, ultimately leading to improved patient outcomes.

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.

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.

Development and approval of novel injectables: enhancing therapeutic innovations.

INTRODUCTION: Novel injectables possess applications in both local and systemic therapeutics delivery. The advancement in utilized materials for the construction of complex injectables has tremendously upgraded their safety and efficacy. AREAS COVERED: This review focuses on various strategies to produce novel injectables, including oily dispersions, in situ forming implants, injectable suspensions, microspheres, liposomes, and antibody-drug conjugates. We herein present a detailed description of complex injectable technologies and their related drug formulations permitted for clinical use by the United States Food and Drug Administration (USFDA). The excipients used, their purpose and the challenges faced during manufacturing such formulations have been critically discussed. EXPERT OPINION: Novel injectables can deliver therapeutic agents in a controlled way at the desired site. However, several challenges persist with respect to their genericization. Astronomical costs incurred by innovator companies during product development, complexity of the product itself, supply limitations with respect to raw materials, intricate manufacturing processes, patent evergreening, product life-cycle extensions, relatively few and protracted generic approvals contribute to the exorbitant prices and access crunch. Moreover, regulatory guidance are grossly underdeveloped and significant efforts have to be directed toward development of effective characterization techniques.

Nanomedicines for intranasal delivery: understanding the nano-bio interactions at the nasal mucus-mucosal barrier.

INTRODUCTION: Intranasal administration is an effective drug delivery routes in modern pharmaceutics. However, unlike other in vivo biological barriers, the nasal mucosal barrier is characterized by high turnover and selective permeability, hindering the diffusion of both particulate drug delivery systems and drug molecules. The in vivo fate of administrated nanomedicines is often significantly affected by nano-biointeractions. AREAS COVERED: The biological barriers that nanomedicines encounter when administered intranasally are introduced, with a discussion on the factors influencing the interaction between nanomedicines and the mucus layer/mucosal barriers. General design strategies for nanomedicines administered via the nasal route are further proposed. Furthermore, the most common methods to investigate the characteristics and the interactions of nanomedicines when in presence of the mucus layer/mucosal barrier are briefly summarized. EXPERT OPINION: Detailed investigation of nanomedicine-mucus/mucosal interactions and exploration of their mechanisms provide solutions for designing better intranasal nanomedicines. Designing and applying nanomedicines with mucus interaction properties or non-mucosal interactions should be customized according to the therapeutic need, considering the target of the drug, i.e. brain, lung or nose. Then how to improve the precise targeting efficiency of nanomedicines becomes a difficult task for further research.

Removal of pharmaceutical compounds and toxicology study in wastewater using Malaysian fungal Ganoderma lucidum.

Elevated usage of pharmaceutical products leads to the accumulation of emerging contaminants in sewage. In the current work, Ganoderma lucidum (GL) was used to remove pharmaceutical compounds (PCs), proposed as a tertiary method in sewage treatment plants (STPs). The PCs consisted of a group of painkillers (ketoprofen, diclofenac, and dexamethasone), psychiatrists (carbamazepine, venlafaxine, and citalopram), beta-blockers (atenolol, metoprolol, and propranolol), and anti-hypertensives (losartan and valsartan). The performance of 800 mL of synthetic water, effluent STP, and hospital wastewater (HWW) was evaluated. Parameters, including treatment time, inoculum volume, and mechanical agitation speed, have been tested. The toxicity of the GL after treatment is being studied based on exposure levels to zebrafish embryos (ZFET) and the morphology of the GL has been observed via Field Emission Scanning Electron Microscopy (FESEM). The findings conclude that GL can reduce PCs from <10% to >90%. Diclofenac and valsartan are the highest (>90%) in the synthetic model, while citalopram and propranolol (>80%) are in the real wastewater. GL effectively removed pollutants in 48 h, 1% of the inoculum volume, and 50 rpm. The ZFET showed GL is non-toxic (LC50 is 209.95 mg/mL). In the morphology observation, pellets GL do not show major differences after treatment, showing potential to be used for a longer treatment time and to be re-useable in the system. GL offers advantages to removing PCs in water due to their non-specific extracellular enzymes that allow for the biodegradation of PCs and indicates a good potential in real-world applications as a favourable alternative treatment.

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.

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.

Drugs in Human Milk Part 1: Practical and Analytical Considerations in Measuring Drugs and Metabolites in Human Milk.

Human milk is a remarkable biofluid that provides essential nutrients and immune protection to newborns. Breastfeeding women consuming medications could pass the drug through their milk to neonates. Drugs can be transferred to human milk by passive diffusion or active transport. The physicochemical properties of the drug largely impact the extent of drug transfer into human milk. A comprehensive understanding of the physiology of human milk formation, composition of milk, mechanisms of drug transfer, and factors influencing drug transfer into human milk is critical for appropriate selection and use of medications in lactating women. Quantification of drugs in the milk is essential for assessing the safety of pharmacotherapy during lactation. This can be achieved by developing specific, sensitive, and reproducible analytical methods using techniques such as liquid chromatography coupled with mass spectrometry. The present review briefly discusses the physiology of human milk formation, composition of human milk, mechanisms of drug transfer into human milk, and factors influencing transfer of drugs from blood to milk. We further expand upon and critically evaluate the existing analytical approaches/assays used for the quantification of drugs in human milk.

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.

In-depth Mechanism, Challenges, and Opportunities of Delivering Therapeutics in Brain Using Intranasal Route.

Central nervous system-related disorders have become a continuing threat to human life and the current statistic indicates an increasing trend of such disorders worldwide. The primary therapeutic challenge, despite the availability of therapies for these disorders, is to sustain the drug's effective concentration in the brain while limiting its accumulation in non-targeted areas. This is attributed to the presence of the blood-brain barrier and first-pass metabolism which limits the transportation of drugs to the brain irrespective of popular and conventional routes of drug administration. Therefore, there is a demand to practice alternative routes for predictable drug delivery using advanced drug delivery carriers to overcome the said obstacles. Recent research attracted attention to intranasal-to-brain drug delivery for promising targeting therapeutics in the brain. This review emphasizes the mechanisms to deliver therapeutics via different pathways for nose-to-brain drug delivery with recent advancements in delivery and formulation aspects. Concurrently, for the benefit of future studies, the difficulties in administering medications by intranasal pathway have also been highlighted.

A sustainable sensitive spectrofluorimetric approach for the determination of the multipotent protein lactoferrin in different pharmaceuticals and infant milk formula: Compliance with greenness metrics.

The current study presents the first spectrofluorimetric approach for the estimation of lactoferrin, depending on the measurement of its native fluorescence at 337 nm after excitation at 230 nm, without the need for any hazardous chemicals or reagents. It was found that the fluorescence intensity versus concentration calibration plot was linear over the concentration range of 0.1-10.0 µg/mL with quantitation and detection limits of 0.082 and 0.027 µg/mL, respectively. The method was accordingly validated according to the ICH recommendations. The developed method was applied for the estimation of lactoferrin in different dosage forms, including capsules and sachets with high percent recoveries (97.84-102.53) and low %RSD values (<1.95). Lactoferrin is one of the key nutrients in milk powder and a significant nutritional fortifier. In order to assess the quality of milk powder, it is essential to rapidly and accurately quantify the lactoferrin content of the product. Therefore, the presented study was successfully applied for the selective estimation of lactoferrin in milk powder with acceptable percent recoveries (96.45-104.92) and %RSD values (≤3.607). Finally, the green profile of the method was estimated using two assessment tools: Green Analytical Procedure Index (GAPI) and Analytical GREEnness (AGREE), which demonstrated its excellent greenness.

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.

A culture-independent nucleic acid diagnostics method for use in the detection and quantification of Burkholderia cepacia complex contamination in aqueous finished pharmaceutical products.

The Burkholderia cepacia complex (Bcc) is the number one bacterial complex associated with contaminated Finished Pharmaceutical Products (FPPs). This has resulted in multiple healthcare related infection morbidity and mortality events in conjunction with significant FPP recalls globally. Current microbiological quality control of FPPs before release for distribution depends on lengthy, laborious, non-specific, traditional culture-dependent methods which lack sensitivity. Here, we present the development of a culture-independent Bcc Nucleic Acid Diagnostic (NAD) method for detecting Bcc contaminants associated with Over-The-Counter aqueous FPPs. The culture-independent Bcc NAD method was validated to be specific for detecting Bcc at different contamination levels from spiked aqueous FPPs. The accuracy in Bcc quantitative measurements was achieved by the high degree of Bcc recovery from aqueous FPPs. The low variation observed between several repeated Bcc quantitative measurements further demonstrated the precision of Bcc quantification in FPPs. The robustness of the culture-independent Bcc NAD method was determined when its accuracy and precision were not significantly affected during testing of numerous aqueous FPP types with different ingredient matrices, antimicrobial preservative components and routes of administration. The culture-independent Bcc NAD method showed an ability to detect Bcc in spiked aqueous FPPs at a concentration of 20 Bcc CFU/mL. The rapid (≤ 4 hours from sample in to result out), robust, culture-independent Bcc NAD method presented provides rigorous test specificity, accuracy, precision, and sensitivity. This method, validated with equivalence to ISO standard ISO/TS 12869:2019, can be a valuable diagnostic tool in supporting microbiological quality control procedures to aid the pharmaceutical industry in preventing Bcc contamination of aqueous FPPs for consumer safety.