Pharmaceutical Methods for Enhancing the Dissolution of Poorly Water-Soluble Drugs.

Low water solubility is the main hindrance in the growth of pharmaceutical industry. Approximately 90% of newer molecules under investigation for drugs and 40% of novel drugs have been reported to have low water solubility. The key and thought-provoking task for the formulation scientists is the development of novel techniques to overcome the solubility-related issues of these drugs. The main intention of present review is to depict the conventional and novel strategies to overcome the solubility-related problems of Biopharmaceutical Classification System Class-II drugs. More than 100 articles published in the last 5 years were reviewed to have a look at the strategies used for solubility enhancement. pH modification, salt forms, amorphous forms, surfactant solubilization, cosolvency, solid dispersions, inclusion complexation, polymeric micelles, crystals, size reduction, nanonization, proliposomes, liposomes, solid lipid nanoparticles, microemulsions, and self-emulsifying drug delivery systems are the various techniques to yield better bioavailability of poorly soluble drugs. The selection of solubility enhancement technique is based on the dosage form and physiochemical characteristics of drug molecules.

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.

[Analysis and Discovery of Functional Biomolecules Using a Combination of Fluorescence Microscopy and Micro- and Nano-devices for Single-molecule Detection].

The author has developed several methodological approaches that use nanophotonic and microfluidic devices to accelerate pharmaceutical research and development. Here, the author describes two of these approaches and provides practical examples. The first is a nanophotonic approach to break the concentration limit of diffusing fluorophore-labeled molecules in single-molecule imaging. Although single-molecule imaging is highly useful in characterizing the kinetics of biomolecular interactions, it requires nanomolar concentrations of labeled molecules in solution. Zero-mode waveguides are nanophotonic structures that reduce the illumination volume by more than three orders of magnitude relative to conventional fluorescence microscopy, thereby allowing single-molecule investigations at micromolar to millimolar concentrations of fluorescent molecules i.e., under near-physiological conditions. The second approach is microfluidic microdroplet-based, allowing the discovery of novel biomolecules with the desired activities. Microfluidics allows the ultrarapid production of monodisperse microdroplets such as water-in-oil microdroplets. Each microdroplet serves as a nano/picoliter-volume test tube, which increases assay sensitivity by increasing the effective concentration of molecules and decreasing the time required to reach detection thresholds. I hope you find this review helpful in your research.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying these sorts of approaches, we propose an additional approach: using AI to augment human intelligence. We have been working on a series of recommendation systems that take advantage of our existing laboratory processes, both wet and computational, in order to provide inspiration to our chemists, suggest next steps in their work, and automate existing workflows. We will describe five such systems in various stages of deployment within the Novartis Institutes for BioMedical Research. While each of these systems addresses different stages of the discovery pipeline, all of them share three common features: a trigger that initiates the recommendation, an analysis that leverages our existing systems with AI, and the delivery of a recommendation. The goal of all of these systems is to inspire and accelerate the drug discovery process.

Lifecycle management in pharmaceutical analysis: How to establish an efficient and relevant continued performance monitoring program.

It is the objective of a systematic and holistic Quality-by-Design approach to demonstrate and ensure that an analytical procedure is fit for its intended purpose over its entire lifecycle. Such a lifecycle approach, as proposed for a new USP General Information Chapter includes the three stages Procedure Design and Development, Procedure Performance Qualification, and Continued Procedure Performance Verification, in alignment to manufacturing process validation. A decisive component of this approach is the Analytical Target Profile, which defines the performance requirements for the measurement of a Quality Attribute as the target for selection, development and optimization of the respective analytical procedures. Although the most benefit can be gained by a comprehensive Quality-by-Design approach establishing the Analytical Target Profile in the very beginning of a drug development project, it may also be established retrospectively for analytical procedures long in routine use, in order to facilitate future lifecycle activities such as continual improvements, transfers, monitoring and periodic performance evaluations. In contrast to the first two stages of the analytical lifecycle with usually limited amount of data, the Continued Procedure Performance Verification stage offers the possibility to utilize a much more reliable data base to collect, analyze, and evaluate data that relate to analytical procedure performance. This monitoring program should be aligned as far as possible with other quality systems already in place and may include performance indicators such as Conformity (i.e. out-of specification test results with analytical root-cause), Validity (i.e. failure to meet method acceptance criteria, e.g. system suitability tests), and (numerical) analytical performance parameters (e.g. ranges for replicate determinations, control sample results, etc). In addition to the monitoring of analytical control parameters by means of control charts, average (pooled) performance parameters can be calculated. Over time, a large number of data can be included and thus the reliability of these estimates is increased tremendously. Such reliable estimates for the true performance parameters, e.g. repeatability or intermediate precision are essential to identify systematic effects (also called special cause variation) with good confidence. The intent of the analytical procedure performance evaluation is to identify substandard performance, identify root cause through investigations, and determine when additional activities are required to improve it. Examples are provided for the monitoring and evaluation of performance parameters for the compendial drug substance Furosemide and for biopharmaceutical applications.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics.

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Optimising medication data collection in a large-scale clinical trial.

OBJECTIVE: Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collected and coded, en masse from the community using this framework. METHODS: The ASPREE framework of type-to-search box with automated coding and linked free text entry was compared to traditional method of free-text only collection and post hoc coding. Reported medications were classified according to their method of collection and analysed by Anatomical Therapeutic Chemical (ATC) group. Relative cost of collecting medications was determined by calculating the time required for database set up and medication coding. RESULTS: Overall, 122,910 participant structured medication reports were entered using the type-to-search box and 5,983 were entered as free-text. Free-text data contributed 211 unique medications not present in the type-to-search box. Spelling errors and unnecessary provision of additional information were among the top reasons why medications were reported as free-text. The cost per medication using the ASPREE method was approximately USD $0.03 compared with USD $0.20 per medication for the traditional method. CONCLUSION: Implementation of this two-pronged framework is a cost-effective alternative to free-text only data collection in community-based research. Higher initial set-up costs of this combined method are justified by long term cost effectiveness and the scientific potential for analysis and discovery gained through collection of detailed, structured medication data.

Remote Controlled Autonomous Microgravity Lab Platforms for Drug Research in Space.

Research conducted in microgravity conditions has the potential to yield new therapeutics, as advances can be achieved in the absence of phenomena such as sedimentation, hydrostatic pressure and thermally-induced convection. The outcomes of such studies can significantly contribute to many scientific and technological fields, including drug discovery. This article reviews the existing traditional microgravity platforms as well as emerging ideas for enabling microgravity research focusing on SpacePharma's innovative autonomous remote-controlled microgravity labs that can be launched to space aboard nanosatellites to perform drug research in orbit. The scientific literature is reviewed and examples of life science fields that have benefited from studies in microgravity conditions are given. These include the use of microgravity environment for chemical applications (protein crystallization, drug polymorphism, self-assembly of biomolecules), pharmaceutical studies (microencapsulation, drug delivery systems, behavior and stability of colloidal formulations, antibiotic drug resistance), and biological research, including accelerated models for aging, investigation of bacterial virulence , tissue engineering using organ-on-chips in space, enhanced stem cells proliferation and differentiation.

Human organotypic bioconstructs from organ-on-chip devices for human-predictive biological insights on drug candidates.

INTRODUCTION: Historically, drug development and marketing failures have been experienced by pharma organizations largely from insufficient human-predictability of biological data. AREAS COVERED: Organs-on-chips (OOCs) are emerging, cutting edge microphysiology systems for in vitro production of microengineered three-dimensional, miniature organotypic constructs obtained by cultivating small amounts of human primary, or induced pluripotent stem, cells in native-like microhabitats. These preparations circumvent experimental limitations inherent to animal assays and two-dimensional monolayers, the mainstay core biological assays of traditional drug research. This report reviews the fundamental tenets, key components (chip plate, biomaterials, cell differentiation approaches, and monitoring sensors) and issues concerning OOC systems (engineered top-down and bottom-up strategies for tissue/organ assembly, public aids to OOC development, regulatory validation, advantages, limitations, prospective and perspective of OOCs, ethics). Examples of OOC platforms (cancer-, lung-, blood-brain barrier-, heart-, intestine-, kidney-, liver-, pharmacokinetics-, placenta and vessel-on-chip) and their importance for drug research and development are presented. EXPERT OPINION: OOC device-generated bioconstructs hold great promise as experimental human tissue and organ platforms for generating human-pertinent knowledge on drug candidates for clinical assessment and reducing reliance on animal models. MPS technologies currently enable ready-to-assemble tissue patches and, hopefully, in coming decades, full-size, patient-personalized organs for regenerative medical interventions.

The Emergence of Universal Chromatographic Methods in the Research and Development of New Drug Substances.

Manufacturing process development of new drug substances in the pharmaceutical industry combines numerous chemical challenges beyond the efficient synthesis of complex molecules. Optimization of a synthetic route involves the screening of multiple reaction variables with a desired outcome that not only depends on an increased product yield but is also highly influenced by the removal efficacy of residual chemicals and reaction byproducts during the subsequent synthetic route. Consequently, organic chemists must survey a wide array of synthetic variables to develop a highly productive, green, and cost-effective manufacturing process. The time constraints of developing robust quantitative methods prior to each processing step can easily lead to sample analysis becoming a bottleneck in synthetic route development. In this regard, conventional "on demand" analytical method development and optimization approaches, traditionally used for guiding synthetic chemistry efforts, become unsustainable. This Account introduces recent efforts to address the aforementioned challenges through the development and implementation of generic or more universal chromatographic methods that can cover a broad spectrum of targeted compound classes. Such generic methods require significant resolving power to enable baseline resolution of multicomponent mixtures in a single experimental run without additional method customization but must be simple enough to allow for routine use by chemists, chemical engineers and other researchers with little experience in chromatographic method development. These powerful analytical methodologies are often employed to minimize the time spent developing new analytical assays, while also facilitating method transfer to manufacturing facilities and application in regulatory settings. Diverse examples of universal and fit-for-purpose analytical procedures are presented herein, illustrating the power of modern readily available analytical technology for streamlining the development of new drug substances in organic chemistry laboratories across both academic and industrial sectors. With recent advances in analytical instrumentation and column technologies, universal chromatographic methods are quickly becoming a proactive and effective strategy to accelerate the discovery and implementation of new synthetic methodologies, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization. Targets of these generic methods include analysis of organic solvents, acid and basic additives, nucleotide species, palladium scavengers, impurity mapping, enantiopurity, synthetic intermediates, active pharmaceutical ingredients and their counterions, dehalogenation byproducts, and mixtures of organohalogenated pharmaceuticals, among other chemicals used or formed in process chemistry reactions.

Pharmaceutical Research for Inherited Metabolic Disorders of the Liver Using Human Induced Pluripotent Stem Cell and Genome Editing Technologies.

Orthotopic liver transplantation, rather than drug therapy, is the major curative approach for various inherited metabolic disorders of the liver. However, the scarcity of donated livers is a serious problem. To resolve this, there is an urgent need for novel drugs to treat inherited metabolic disorders of the liver. This requirement, in turn, necessitates the establishment of suitable disease models for many inherited metabolic disorders of the liver that currently lack such models for drug development. Recent studies have shown that human induced pluripotent stem (iPS) cells generated from patients with inherited metabolic disorders of the liver are an ideal cell source for models that faithfully recapitulate the pathophysiology of inherited metabolic disorders of the liver. By using patient iPS cell-derived hepatocyte-like cells, drug efficacy evaluation and drug screening can be performed. In addition, genome editing technology has enabled us to generate functionally recovered patient iPS cell-derived hepatocyte-like cells in vitro. It is also possible to identify the genetic mutations responsible for undiagnosed liver diseases using iPS cell and genome editing technologies. Finally, a combination of exhaustive analysis, iPS cells, and genome editing technologies would be a powerful approach to accelerate the identification of novel genetic mutations responsible for undiagnosed liver diseases. In this review, we will discuss the usefulness of iPS cell and genome editing technologies in the field of inherited metabolic disorders of the liver, such as alpha-1 antitrypsin deficiency and familial hypercholesterolemia.

Applications of proteomics in pharmaceutical research and development.

The significance of proteomics in the pharmaceutical industry has increased since overcoming initial difficulties. This review discusses recent proteomics publications from pharmaceutical companies to identify new trends in proteomics applications to research and development. Applications of proteomics such as chemical proteomics, protein expression profiling, targeted protein quantitation, analysis of protein-protein interactions and post-translational modification are widely used by various sections of the industry. Technological advancements in proteomics will further accelerate pharmaceutical research and development.

Design of Experiments in metabolomics-related studies: An overview.

Nowadays, Design of Experiments (DoE) approach is a very popular methodology of planning and conducting experiments, where the effect of each tested factor on the studied responses is systematically examined and documented. The results obtained in such manner represent the design space more precisely than in the case of One-Variable-At-Time (OVAT) approach, leading to reliable and comprehensive results, while saving time and resources. Despite such a large increase of interest in this approach recently, its implementation in metabolomics research seems to be limited. Therefore, in this short overview, apart from summarizing some basic concepts of DoE, we wanted to provide a guideline for those who are about to plan metabolomics-related experiments. This overview is divided into four sections. In addition to the first section, which will introduce the history and basics of DoE, second part will provide concise description of the most popular experimental designs. Furthermore, third section will describe examples of DoE application in metabolomics and related studies. We will conclude with fourth section, providing you briefly with opportunities and trends in metabolomics research utilizing experimental design.

Prediction of Human Distribution Volumes of Compounds in Various Elimination Phases Using Physiologically Based Pharmacokinetic Modeling and Experimental Pharmacokinetics in Animals.

Predicting the pharmacokinetics of compounds in humans is an important part of the drug development process. In this study, the plasma concentration profiles of 10 marketed compounds exhibiting two-phase elimination after intravenous administration in humans were evaluated in terms of distribution volumes just after intravenous administration (V 1), at steady state (V ss), and in the elimination phase (Vß ) using physiologically based pharmacokinetic (PBPK) modeling implemented in a commercially available simulator (Simcyp). When developing human PBPK models, the insight gained from prior animal PBPK models based on nonclinical data informed the optimization of the lipophilicity input of the compounds and the selection of the appropriate mechanistic tissue partition methods. The accuracy of V 1, V ss, and Vß values predicted that using human PBPK models developed in accordance with prior animal PBPK models was superior to using those predicted using conventional approaches, such as allometric scaling, especially for V 1 and Vß By conventional approaches, the V 1 and Vß values of 4-5 of 10 compounds were predicted within a 3-fold error of observed values, whereas V ss values for their majority were predicted as such. PBPK models predicted V 1, V ss, and Vß values for almost all compounds within 3-fold errors, resulting in better predictions of plasma concentration profiles than allometric scaling. The distribution volumes predicted using human PBPK models based on prior animal PBPK modeling were more accurate than those predicted without reference to animal models. This study demonstrated that human PBPK models developed with consideration of animal PBPK models could accurately predict distribution volumes in various elimination phases.