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.

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.

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.

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.

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs.

PURPOSE: Volume of distribution at steady state (Vdss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vdss can be estimated using in silico mechanistic methods or empirically scaled from Vdss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs). METHODS: For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vdss. The software used for predicting human tissue partition coefficients and Vdss based on the method described by Rodgers and Rowland is made available as supporting information. RESULTS: This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species. CONCLUSIONS: These results illustrate the great potential of mechanistic in silico models to accurately predict Vdss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vdss in preclinical species could be obtained by a similar process.

Use of QSPR Modeling to Characterize In Vitro Binding of Drugs to a Gut-Restricted Polymer.

PURPOSE: Polymeric drugs, including patiromer (Veltassa®), bind target molecules or ions in the gut, allowing fecal elimination. Non-absorbed insoluble polymers, like patiromer, avoid common systemic drug-drug interactions (DDIs). However, the potential for DDI via polymer binding to orally administered drugs during transit of the gastrointestinal tract remains. Here we elucidate the properties correlated with drug-patiromer binding using quantitative structure-property relationship (QSPR) models. METHODS: We selected 28 drugs to evaluate for binding to patiromer in vitro over a range of pH and ionic conditions intended to mimic the gut environment. Using this in vitro data, we developed QSPR models using step-wise linear regression and analyzed over 100 physiochemical drug descriptors. RESULTS: Four descriptors emerged that account for ~70% of patiromer-drug binding in vitro: the computed surface area of hydrogen bond accepting atoms, ionization potential, electron affinity, and lipophilicity (R 2 = 0.7, Q 2 = 0.6). Further, certain molecular properties are shared by nonbinding, weak, or strong binding compounds. CONCLUSIONS: These findings offer insight into drivers of in vitro binding to patiromer and describe a useful approach for assessing potential drug-binding risk of investigational polymeric drugs.

Cocrystal Solubility Product Prediction Using an in combo Model and Simulations to Improve Design of Experiments.

PURPOSE: To predict the aqueous solubility product (K sp ) and the solubility enhancement of cocrystals (CCs), using an approach based on measured drug and coformer intrinsic solubility (S 0API , S 0cof ), combined with in silico H-bond descriptors. METHOD: A regression model was constructed, assuming that the concentration of the uncharged drug (API) can be nearly equated to drug intrinsic solubility (S 0API ) and that the concentration of the uncharged coformer can be estimated from a linear combination of the log of the coformer intrinsic solubility, S 0cof , plus in silico H-bond descriptors (Abraham acidities, α, and basicities, ß). RESULTS: The optimal model found for n:1 CCs (-log10 form) is pK sp = 1.12 n pS 0API + 1.07 pS 0cof + 1.01 + 0.74 αAPI·ßcof - 0.61 ßAPI; r 2 = 0.95, SD = 0.62, N = 38. In illustrative CC systems with unknown K sp , predicted K sp was used in simulation of speciation-pH profiles. The extent and pH dependence of solubility enhancement due to CC formation were examined. Suggestions to improve assay design were made. CONCLUSION: The predicted CC K sp can be used to simulate pH-dependent solution characteristics of saturated systems containing CCs, with the aim of ranking the selection of coformers, and of optimizing the design of experiments.

Non-profit Drug Research and Development at a Crossroads.

In wealthy nations, non-profit drug R&D has been proposed to reduce the prices of medicines. We sought to review the ethical and economic issues concerning non-profit drug R&D companies, and the possible impact that their pricing strategy may have on the innovation efforts from for-profit companies targeting the same segment of the pharmaceutical market. There are two possible approaches to pricing drugs developed by non-profit R&D programs: pricing that maximises profits and "affordable" pricing that reflects the cost of manufacturing and distribution, plus a margin that ensures sustainability of the drug supply. Overall, the non-profits face ethical challenges - due to the lack of resources, they are unable to independently commercialize their products on a large scale; however, the antitrust law does not permit them to impose prices on potential licensees. Also, reduced prices for the innovative products may result in drying the for-profit R&D in the area.

Development and in vitro evaluation of pH-independent release matrix tablet of weakly acidic drug valsartan using quality by design tools.

The main objective of this study was the development of pH-independent controlled release valsartan matrix tablet in Quality by design (QbD) framework. The quality target product profile (QTPP), critical quality attributes (CQAs) and critical material attributes (CMAs) were defined by science and risk-based methodologies. Potential risk factors were identified with Fishbone diagram. Following, CMAs were further investigated with a semi-quantitative risk assessment method, which has been revised with mitigated risks after development and optimization studies. According to defined critical material attributes, which one of them was determined to be the dissolution, formulation optimization study was performed by using a statistical design of experiment. Formulation variables have been identified and fixed first with a 'One factor at a time (OFAT)' approach. After OFAT studies, a statistical experimental design was conducted with the most critical material attributes. Statistical design space and mathematical prediction equations have been developed for dissolution and hardness, which is important to predict drug dissolution behavior. In conclusion, a pH-independent release has been achieved for weakly acidic drug valsartan with a deeper understanding of drug product quality, with the science and risk-based approaches of QbD tools.

Clinical research in neonates and infants: Challenges and perspectives.

To date, up to 65% of drugs used in neonates and infants are off-label or unlicensed, as they were implemented in clinical care without the usual regulatory phases of pharmacological drug development. Pharmacotherapy in this age group is still mainly based on the individual clinical expertise of specialized pediatricians. Pharmacological trials involving neonates are indeed more difficult to perform: appropriate dosing is hampered by the rapid physiological changes occurring at this stage of development, and the selection of proper end-points and biomarkers is complicated by the limited knowledge of the pathophysiology of the specific diseases of infancy. Moreover, there are many ethical challenges in planning and conducting drug studies in pediatric patients (especially in newborns and infants). In the current review, we address some challenges and discuss possible perspectives to stimulate scientific and clinical pharmacological research in neonates and infants. We hereby aim to illustrate the add on value of the regulatory framework for model-based neonatal medicinal development currently used in Europe and the United States. We provide several examples of successful recent pharmacological trials performed in neonates and infants. In these examples, success was ensured by the implementation of specific pharmacokinetic assessments, thanks to accurate drug dosing achieved with a combination of dose validation, population pharmacokinetics and mathematical models of drug clearance and distribution; moreover, age-specific pharmacodynamics was considered via appropriate evaluations of drug efficacy with end-points adapted to the peculiar pathophysiology of diseases in this age group. These "pharmacological" challenges add to the ethical challenges that are always present in planning and conducting clinical studies in neonates and infants and support the opinion that clinical research in pediatrics should be evaluated by ad hoc ethical committees with specific expertise.

The Next Era: Deep Learning in Pharmaceutical Research.

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Applicability of gas chromatography/quadrupole-Orbitrap mass spectrometry in support of pharmaceutical research and development.

RATIONALE: Gas chromatography/mass spectrometry (GC/MS) is a fundamental tool used to identify impurities throughout the active pharmaceutical ingredients development process. The coupling of Orbitrap mass spectrometry with GC marks an exciting advance in capability for GC/MS, offering a significant step change in resolving power, mass accuracy, sensitivity and linear range. METHODS: A range of pharmaceutically relevant samples representing typical starting materials has been investigated with particular reference to impurity identification. The mass accuracy in Electron Ionisation (EI) and Chemical Ionisation (CI) was investigated for impurity identification. The linearity and mass accuracy over a wide dynamic range were evaluated. The number of scans obtained across chromatographic peaks was assessed at various resolution settings from 15,000 to 120,000 (full width at half maximum (FWHM) at m/z 200). RESULTS: All the accurate mass measurements for impurities were within <1 ppm of the theoretical m/z value. The scan speed at the highest resolution produced 11 scans across the peak, and the mass accuracy for all scans was consistently <1 ppm - sufficient for impurity investigations and quantitative analysis. Linearity was demonstrated for N,N,N'-trimethylethylenediamine over a concentration range of 0.0001 to 0.1250 µg/mL (w/v) with a correlation coefficient R(2) = 0.9996 and mass accuracy across all concentrations at <1.1 ppm. CONCLUSIONS: GC/Orbitrap MS has been evaluated for both qualitative and quantitative analysis of typical pharmaceutical precursors and impurities. Accurate mass measurement across a wide dynamic range, linearity and the ability to identify impurities in EI and CI illustrate that this instrument is a powerful tool of great benefit to pharmaceutical analysis.

[Benefits of large healthcare databases for drug risk research]. / Der Nutzen großer Gesundheitsdatenbanken für die Arzneimittelrisikoforschung.

Large electronic healthcare databases have become an important worldwide data resource for drug safety research after approval. Signal generation methods and drug safety studies based on these data facilitate the prospective monitoring of drug safety after approval, as has been recently required by EU law and the German Medicines Act. Despite its large size, a single healthcare database may include insufficient patients for the study of a very small number of drug-exposed patients or the investigation of very rare drug risks. For that reason, in the United States, efforts have been made to work on models that provide the linkage of data from different electronic healthcare databases for monitoring the safety of medicines after authorization in (i) the Sentinel Initiative and (ii) the Observational Medical Outcomes Partnership (OMOP). In July 2014, the pilot project Mini-Sentinel included a total of 178 million people from 18 different US databases. The merging of the data is based on a distributed data network with a common data model. In the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) there has been no comparable merging of data from different databases; however, first experiences have been gained in various EU drug safety projects. In Germany, the data of the statutory health insurance providers constitute the most important resource for establishing a large healthcare database. Their use for this purpose has so far been severely restricted by the Code of Social Law (Section 75, Book 10). Therefore, a reform of this section is absolutely necessary.

Establishment of human induced pluripotent stem cell-derived hepatobiliary organoid with bile duct for pharmaceutical research use.

In vitro models of the human liver are promising alternatives to animal tests for drug development. Currently, primary human hepatocytes (PHHs) are preferred for pharmacokinetic and cytotoxicity tests. However, they are unable to recapitulate the flow of bile in hepatobiliary clearance owing to the lack of bile ducts, leading to the limitation of bile analysis. To address the issue, a liver organoid culture system that has a functional bile duct network is desired. In this study, we aimed to generate human iPSC-derived hepatobiliary organoids (hHBOs) consisting of hepatocytes and bile ducts. The two-step differentiation process under 2D and semi-3D culture conditions promoted the maturation of hHBOs on culture plates, in which hepatocyte clusters were covered with monolayered biliary tubes. We demonstrated that the hHBOs reproduced the flow of bile containing a fluorescent bile acid analog or medicinal drugs from hepatocytes into bile ducts via bile canaliculi. Furthermore, the hHBOs exhibited pathophysiological responses to troglitazone, such as cholestasis and cytotoxicity. Because the hHBOs can recapitulate the function of bile ducts in hepatobiliary clearance, they are suitable as a liver disease model and would be a novel in vitro platform system for pharmaceutical research use.

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.