Early Stages Of Drug Development Research Articles

How organ-on-a-chip is advancing cancer research and oncology - a cancer hallmarks’ perspective

Despite significant improvements in methodologies behind the development of new anticancer therapies, the path from early-stage drug development through preclinical and clinical development pipeline is still arduous. Further refinement of the current preclinical models and the development of complementing alternative techniques that enable more reliable studies are paramount. The emerging organ-on-a-chip (OoC) technologies are physiological-like organ biomimetic systems built on a microfluidic chip, capable of enabling precise control over various physicochemical and biomechanical parameters and helping recreate the natural physiology and mechanical forces that cells experience in the human body. In oncology research, especially, since cancer has been understood to be a dynamic disease featured by complex interactions between cancer cells and their environment, this technology offers added advantage as it is able to provide a dynamic platform to simulate cancer-on-a-chip emulating the biological context of tumor microenvironment (TME); demonstrating progression to metastases to multiorgans; and helping to unravel complex information that other current in vitro methods are otherwise not able to provide. In this contribution, we provide a biological perspective on the recent advances in the field of OoC models in cancer biology studies from a cancer hallmark perspective. We also discuss current prospects and funding opportunities in this space, as well as a possible future outlook from a biology perspective, including major challenges and new opportunities as a way forward to OoC technologies.

NeuroClick: Advanced Software for Designing Blood-Brain Barrier-Permeable Drugs Using Click Reaction Simulations.

NeuroClick is a software tool designed for the in silico execution of azide-alkyne cycloaddition reactions, commonly known as click reactions. We developed this graphical user interface application to expedite the drug discovery process by generating libraries of 1,2,3-triazole compounds. NeuroClick enables users to input reagent SMILES strings, rapidly generating and screening extensive combinatorial libraries at a pace of 10,000 molecules per minute. The software applies stringent criteria to ensure the relevance and accuracy of the generated compounds, excluding molecules without azide groups or those with multiple reactive functional groups to maintain dataset integrity. NeuroClick incorporates advanced filtering options based on Lipinski's rule of five and blood-brain barrier (BBB) permeability predictors, allowing researchers to identify drug-like molecules with potential central nervous system activity. The software's high-throughput and user-friendly interface significantly enhance the efficiency of early-stage drug development by facilitating the exploration of vast chemical spaces and identifying promising lead compounds for further development. This article provides comprehensive guidance on the installation, usage, and features of NeuroClick, ensuring that users can leverage its full potential in their research endeavors. © 2024 Wiley Periodicals LLC. Basic Protocol: NeuroClick workflow for generating BBB-permeating drugs.

Boronic Acid-Based Glucose Detection for Liver Three-Dimensional Cellular Models.

Liver 3D cell models are regularly employed as a screening platform for predicting the metabolic safety of drugs, by monitoring the physiological responses of the spheroids, through the measurement of relevant markers of normal liver physiology, notably glucose. Measuring glucose levels within the spheroids and their surroundings provides insight into the metabolic homeostasis of liver cells and may be employed as an indication of potential drug-induced toxicity. Several ortho-aminomethyl phenylboronic acid (PDBA) glucose sensors have been developed. Most recently, Mc-CDBA ((((((2-(methoxycarbonyl)anthracene-9,10-diyl)bis(methylene)) bis(methylazanediyl))bis(methylene))bis(4-cyano-2,1-phenylene))diboronic acid) was reported. Although Mc-CDBA exhibits good water solubility and sensitivity toward glucose, its ability for intra- and extracellular glucose monitoring in spheroids has not been determined. Here, we present a set of Mc-CDBA derivatives: carboxylic (BA) and amide (BA 5)-based Mc-CDBA sensors for extra- and intracellular glucose monitoring, respectively. Both sensors exhibit superior spectroscopic features. BA 5 showed selective intracellular accumulation in liver spheroids and exhibited more than 3-fold higher basal fluorescence sensitivity compared to Mc-CDBA. These observations led to the development of an extracellular hydrogel-embedded sensor (HG-BA 21) to monitor extracellular glucose levels under persistent solution flow mimicking physiological conditions. We have therefore demonstrated that the sensors developed by our team are suitable for a variety of assays, notably with liver spheroids, and provide powerful new tools for organ-on-a-chip applications predicting drug-induced liver injury in the early stages of drug development.

Quantitative Prediction of Drug-Drug Interactions Caused by CYP3A Induction Using Endogenous Biomarker 4β-Hydroxycholesterol.

Evaluation of the CYP3A induction risk is important in early drug development stages. This study focused on 4β-hydroxycholesterol (4β-HC) as an endogenous biomarker of drug-drug interactions (DDIs) caused by CYP3A induction. We investigated a new approach using 4β-HC for quantitative prediction of DDIs caused by CYP3A induction based on the mechanistic static pharmacokinetic (MSPK) model. The induction ratio, i.e., the ratio of plasma 4β-HC or 4β-HC/cholesterol (4β-HC/C) with and without a coadministered CYP3A inducer, and the ratio of the area under the plasma concentration-time curve (AUCR), i.e., the ratio of the AUC of plasma CYP3A substrate drugs with and without a coadministered CYP3A inducer, were collected. The scaling factor (d) in the MSPK model was calculated from the induction ratio of 4β-HC or 4β-HC/C based on the systemic term in the MSPK model. The AUCR of 18 CYP3A substrates with and without coadministration of seven CYP3A inducers were then predicted by substituting the calculated d value into the MSPK model. This approach showed that approximately 84% of the predicted AUCR values were within a twofold range of the observed values, showing that this approach can be a good tool to quantitatively predict DDIs caused by CYP3A induction. SIGNIFICANCE STATEMENT: A concise approach to predict drug interactions with adequate accuracy is preferable in the early drug development stage. In this study, a new approach using 4β-hydroxycholesterol for quantitative prediction of drug-drug interactions caused by CYP3A induction was investigated. The predictability was verified using seven CYP3A inducers and 18 substrates.

WaSPred: A reliable AI-based water solubility predictor for small molecules

A rapid and reliable evaluation of the aqueous solubility of small molecules is a hot topic for the scientific community and represents a field of particular interest in drug discovery. In fact, aqueous solubility significantly impacts various aspects that collectively influence a drug’s overall pharmacokinetics, including absorption, distribution and metabolism. For this reason, in silico approaches that provide fast and cost-effective solubility predictions, can serve as invaluable tools in the early stages of drug development. Although additional molecular features should be considered, accurate solubility predictions can help medicinal chemists rationally planning the synthesis of compounds more likely to exhibit desirable pharmacokinetic properties and in selecting the most promising candidates for further biological testing (e.g., cellular assays) from an initial pool of hit compounds with detected preliminary bioactivity. In this context, we herein report the development and evaluation of WaSPred, our AI-based water solubility predictor for small molecules. WaSPred not only showed high reliability in water solubility predictions performed on structurally heterogeneous compounds, belonging to multiple external datasets, but also demonstrated superior performance compared to a set of other commonly used water solubility predictors, thus confirming its state-of the-art robustness and its usefulness as an in silico approach for water solubility evaluations..

In Vitro Evaluation of Pharmacokinetic Properties of Selected Dual COX-2 and 5-LOX Inhibitors.

Evaluation of pharmacokinetic properties is a significant step at the early stages of drug development. In this study, an in vitro evaluation of the pharmacokinetic properties of five newly synthesized compounds was performed. These compounds belong to N-hydroxyurea and hydroxamic acid derivatives and analogs of NSAIDs indomethacin, flurbiprofen, diclofenac, ibuprofen, and naproxen (compounds 1, 2, 3, 11, and 12, respectively) with dual COX-2 and 5-LOX inhibitory activity. Two in vitro methods (biopartitioning micellar chromatography (BMC) and PAMPA) were used to evaluate passive gastrointestinal absorption, while high-performance affinity chromatography (HPAC) and differential pulse voltammetry (DPV) were used to evaluate binding to human serum albumin (HSA). The introduction of N-hydroxyurea and hydroxamic acid groups into the structure of NSAIDs decreases both expected passive gastrointestinal absorption (BMC k values were from 3.02 to 9.50, while for NSAIDs were from 5.29 to 13.36; PAMPA -logPe values were between 3.81 and 4.76, while for NSAIDs were ≤3.46) and HSA binding (HPAC logk values were from 2.03 to 9.54, while for NSAIDs were ≥11.03; DPV peak potential shifts were between 7 and 34, while for NSAIDs were ≥54). Structural modifications of all tested compounds that increase lipophilicity could be considered to enhance their passive gastrointestinal absorption. Considering lower expected HSA binding and higher lipophilicity of tested compounds compared to corresponding NSAIDs, it can be expected that the volume of distribution of compounds 1, 2, 3, 11, and 12 will be higher. Reduced HSA binding may also decrease interactions with other drugs in comparison to corresponding NSAIDs. All tested compounds showed significant microsomal instability (25.07-58.44% decrease in concentration) in comparison to indomethacin (14.47%) and diclofenac (20.99%).

Widespread loss-of-function mutations implicating preexisting resistance to new or repurposed anti-tuberculosis drugs

BackgroundFive New or Repurposed Drugs (NRDs) were approved in the last decade for treatment of multi-drug resistant tuberculosis: bedaquiline, clofazimine, linezolid, delamanid, and pretomanid. Unfortunately, resistance to these drugs emerged faster than anticipated, potentially due to preexisting resistance in naïve strains. Previous investigations into the rapid emergence have mostly included short variants. For the first time, we utilize de novo-assembled genomes, and systematically include Structural Variations (SV) and heterogeneity to comprehensively study this rapid emergence. We show high prevalence of preexisting resistance, identify novel markers of resistance, and lay the foundation for preventing preexisting resistance in future drug development. MethodsFirst, a systematic literature review revealed 313 NRD resistance variants in 13 genes. Next, 409 globally diverse clinical isolates collected prior to the drugs’ programmatic use (308 were multidrug resistant, 106 had de novo assembled genomes) were utilized to study the 13 genes comprehensively for conventional, structural, and heterogeneous variants. FindingsWe identified 5 previously reported and 67 novel putative NRD resistance variants. These variants were 2 promoter mutations (in 8/409 isolates), 13 frameshifts (21/409), 6 SVs (9/409), 35 heterogeneous frameshifts (32/409) and 11 heterogeneous SVs (12/106). Delamanid and pretomanid resistance mutations were most prevalent (48/409), while linezolid resistance mutations were least prevalent (8/409). InterpretationPreexisting mutations implicated in resistance to at least one NRD was highly prevalent (85/409, 21 %). This was mostly caused by loss-of-function mutations in genes responsible for prodrug activation and efflux pump regulation. These preexisting mutations may have emerged through a bet-hedging strategy, or through cross-resistance with non-tuberculosis drugs such as metronidazole. Future drugs that could be resisted through loss-of-function in non-essential genes may suffer from preexisting resistance. The methods used here for comprehensive preexisting resistance assessment (especially SVs and heterogeneity) may mitigate this risk during early-stage drug development.

Temperature mapping of milling by dual centrifugation: A systematic investigation

Using low quantities of drug compounds is often favorable in the early stages of drug development, especially for what require a large screening investigation to define the final formulation composition, such as nano- and microsuspensions. For that reason, the dual centrifugation approach has in the recent years been used due to its reproducible and fast-milling capacity with 40 samples in 2 mL vials simultaneously without the addition of cooling breaks due to a built-in cooling system. Nonetheless, heat can be dissipated into the samples during high-intensity milling, resulting in increased sample temperatures that potentially can affect thermolabile compounds and potential influence the obtained suspensions in the screening experiments if the used stabilizer has temperature dependent variations in the performance. Hence, a systematic investigation of the influence of different process parameters on the heat dissipation in samples during milling by the dual centrifugation approach was performed in the present study. It was found that the milling speed had the highest impact on the final sample temperature, but also other parameters, such as the bead loading, bead size, and placement in the centrifuge during milling had significantly influenced the final mean temperature of the milling media. Higher temperatures were obtained with higher bead loadings, i.e., 3000 mg milling beads/mL and milling speeds (1500 rpm), and when smaller milling beads, i.e., 0.1 mm, were used during production. The study further showed that higher temperatures were measured for samples located on the bottom disk during milling, and also when located on the outer placement on the sample disk. Upscale investigations showed immensely increased sample temperatures (almost up to boiling point) when samples were prepared under similar formulation parameters and milling speed as small-volume vials. Furthermore, the study indicated that the addition of drug compounds during suspension preparation decreased the final sample temperature compared to samples that only contained purified water due to energy absorption of the drug compound.

Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models.

Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxic metabolites and drug-drug interactions. Therefore, it is of high importance to predict if a compound is the substrate of a given P450 in the early stage of drug development. In this study, we built the multitask learning models to simultaneously predict the substrates of five major drug-metabolizing P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the collected substrate data sets. Compared to the single-task model and conventional machine learning models, the multitask fingerprints and graph neural networks model achieved superior performance with the average AUC values of 90.8% on the test set. Notably, the multitask model demonstrated its good performance on the small amount of substrate data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley additive explanation and the attention mechanism were used to reveal specific substructures associated with P450 substrates, which were further confirmed and complemented by the substructure mining tool and the literature.

Development and characterization of dendritic cell internalization and activation assays contributing to the immunogenicity risk evaluation of biotherapeutics.

Immunogenicity refers to the ability of a substance, such as a therapeutic drug, to elicit an immune response. While beneficial in vaccine development, undesirable immunogenicity can compromise the safety and efficacy of therapeutic proteins by inducing anti-drug antibodies (ADAs). These ADAs can reduce drug bioavailability and alter pharmacokinetics, necessitating comprehensive immunogenicity risk assessments starting at early stages of drug development. Given the complexity of immunogenicity, an integrated approach is essential, as no single assay can universally recapitulate the immune response leading to the formation of anti-drug antibodies. To better understand the Dendritic Cell (DC) contribution to immunogenicity, we developed two flow cytometry-based assays: the DC internalization assay and the DC activation assay. Monocyte-derived dendritic cells (moDCs) were generated from peripheral blood mononuclear cells (PBMCs) and differentiated over a five-day period. The internalization assay measured the accumulation rate of therapeutic antibodies within moDCs, while the activation assay assessed the expression of DC activation markers such as CD40, CD80, CD86, CD83, and DC-SIGN (CD209). To characterize these two assays further, we used a set of marketed therapeutic antibodies. The study highlights that moDCs differentiated for 5 days from freshly isolated monocytes were more prone to respond to external stimuli. The internalization assay has been shown to be highly sensitive to the molecule tested, allowing the use of only 4 donors to detect small but significant differences. We also demonstrated that therapeutic antibodies were efficiently taken up by moDCs, with a strong correlation with their peptide presentation on MHC-II. On the other hand, by monitoring DC activation through a limited set of activation markers including CD40, CD83, and DC-SIGN, the DC activation assay has the potential to compare a series of compounds. These two assays provide a more comprehensive understanding of DC function in the context of immunogenicity, highlighting the importance of both internalization and activation processes in ADA development. The DC internalization and activation assays described here address key gaps in existing immunogenicity assessment methods by providing specific and reliable measures of DC function. The assays enhance our ability to pre-clinically evaluate the immunogenic potential of biotherapeutics, thereby improving their safety and efficacy. Future work should focus on further validating these assays and integrating them into a holistic immunogenicity risk assessment framework.

Graph Association Analyses for Early Drug Discovery

We demonstrate MedHunter, a system for assisting the early stage of drug development. MedHunter builds a biomedical knowledge graph DDKG by integrating data from eleven biochemical libraries and data banks, and aligning entities from different data sources by means of heterogeneous entity resolution. It identifies drug-disease associations and protein-protein interactions in DDKG by employing graph association rules (GARs). GARs use graph patterns to extract relevant entities and embed ML models as predicates. MedHunter discovers GARs from DDKG and incrementally enriches DDKG with external data; it cleans DDKG with a special form of GARs. We demonstrate MedHunter for its (a) interfaces, (b) data enrichment/cleaning, and (c) applications in target identification, drug-drug interaction and protein-protein interaction.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.

Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

A Radioactivity-mass Spectrometry Calibration Method Coupled with Biosynthesis to Generate a Metabolite Standard for Enzyme Kinetics Studies

In early stages of drug development, the absence of authentic metabolite standards often results in semi-quantitative measurements of metabolite formation in reaction phenotyping studies using mass spectrometry (MS), leading to inaccuracies in the determination of enzyme kinetic parameters, such as the Michaelis constant (Km). Moreover, it is impossible to ascertain the maximum rate of enzyme-catalyzed reactions (kcat or Vmax). The use of radiolabeled parent compounds can circumvent this problem. However, radiometric detection exhibits significantly lower sensitivity compared to MS. To address these challenges, we have developed a stepwise approach that leverages biosynthesized radiolabeled and non-radiolabeled metabolites as standards, enabling accurate determination of Km, kcat or Vmax without the need for authentic metabolite standards. This approach, using the carbon-14 [14C] labeled metabolite to calibrate the unlabeled metabolite (14C calibration method), combines radiometric with LC-MS/MS detection to generate both [14C]-labeled and unlabeled metabolite standard curves to ensure that the sample concentrations measured are accurately quantitated. Two case studies were presented to demonstrate the utility of this method. We first compared the accuracy of the 14C calibration method to the use of authentic standards for quantitating imipramine metabolites. Next, we biosynthesized and quantitated the metabolites of BI 894416 using 14C calibration method and evaluated the enzyme kinetics of metabolite formation. The Km values of the metabolite formation demonstrated substantially improved accuracy compared to MS semi-quantitation. Moreover, the 14C calibration method offers a streamlined approach to prepare multiple metabolite standards from a single biosynthesis, reducing the time required for structure elucidation and metabolite synthesis.

Revolutionizing Healthcare and Drug Discovery: The Impact of Artificial Intelligence on Pharmaceutical Development

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery

Daniel H. Wolf: Understanding motivation impairment from clinical, behavioral, and neurobiological perspectives to pave the way for better treatments

Dr. Daniel Wolf grew up in Los Angeles, completed his BA at Harvard College, MD-PhD at Yale University, psychiatry residency at MGH-McLean, and neuropsychiatry fellowship at UPenn. He is an Associate Professor of Psychiatry at UPenn's Perelman School of Medicine. As head of the Laboratory for Motivation in Psychiatry, Dr. Wolf uses functional neuroimaging to study the neural mechanisms of amotivation and other symptom dimensions in psychosis and at-risk states, aiming to develop novel assessment biomarkers for early-stage drug development. Dr. Wolf also provides outpatient care to individuals with psychosis while supervising clinical trainees. As Director of the Clinical Neurosciences Training Program and Co-Director of the Psychosis T32, he provides seminars and mentoring to medical and graduate students and postdoctoral fellows; he earned the School of Medicine's teaching awards in both Basic Science and Translational Science. He is also an ACNP Fellow and member of the Neuropsychopharmacology editorial board. Dr. Wolf answered the Genomic Press Interview, providing our readers with reflections on his life and career.

Experimental models for peri-implant diseases: a narrative review.

Peri-implant diseases, being the most common implant-related complications, significantly impact the normal functioning and longevity of implants. Experimental models play a crucial role in discovering potential therapeutic approaches and elucidating the mechanisms of disease progression in peri-implant diseases. This narrative review comprehensively examines animal models and common modeling methods employed in peri-implant disease research and innovatively summarizes the in vitro models of peri-implant diseases. Articles published between 2015 and 2023 were retrieved from PubMed/Medline, Web of Science, and Embase. All studies focusing on experimental models of peri-implant diseases were included and carefully evaluated. Various experimental models of peri-implantitis have different applications and advantages. The dog model is currently the most widely utilized animal model in peri-implant disease research, while rodent models have unique advantages in gene knockout and systemic disease induction. In vitro models of peri-implant diseases are also continuously evolving to meet different experimental purposes. The utilization of experimental models helps simplify experiments, save time and resources, and promote advances in peri-implant disease research. Animal models have been proven valuable in the early stages of drug development, while technological advancements have brought about more predictive and relevant in vitro models. This review provides clear and comprehensive model selection strategies for researchers in the field of peri-implant diseases, thereby enhancing understanding of disease pathogenesis and providing possibilities for developing new treatment strategies.

Simultaneous Protein Adsorption and Viscosity Measurement using Micropillar-Enhanced Acoustic Wave (μPAW) Device for Pharmaceutical Applications

At the early stages of drug development, the amount of drug materials is rather limited. In this case, viscosity measurement is often postponed to the later stages, where grams of proteins can be produced. Therefore, it is necessary to develop a viscometer capable of measuring the viscosity with high accuracy while requiring low sample volume. This study presents a novel viscosity measurement technique based on measuring the resonance frequency and motional resistance of a micropillar-enhanced acoustic wave (μPAW) device. The μPAW was developed by fabricating micropillars on the quartz crystal microbalance substrate in order to achieve ultra-high sensitivity, thanks to a unique coupling between the micropillar and the resonator. The experimental measurements demonstrated a nonlinear relationship between the density and viscosity of the fluid and the response of μPAW. A calibration correlation was developed using the response of μPAW in aqueous glycerol and sucrose solutions. The measurements were then extended using high-concentration BSA solutions as the model of protein solution. The main advantage of the μPAW device in this work over other viscometers is the ability to simultaneously measure solution viscosity and protein adsorption on the surface. This is a huge step forward in the development of sensing systems for the pharmaceutical industry, where real-time sensing of target biological proteins and measuring the viscosity of a solution is required.

Formation of functional, extended bile canaliculi, and increased bile acid production in sandwich-cultured human cryopreserved hepatocytes using commercially available culture medium

Drug-induced cholestasis results in drug discontinuation and market withdrawal, and the prediction of cholestasis risk is critical in the early stages of drug development. Animal tests and membrane vesicle assay are currently being conducted to assess the risk of cholestasis in the preclinical stage. However, these methods have drawbacks, such as species differences with humans and difficulties in evaluating the effects of drug metabolism and other transporters, implying the need for a cholestasis risk assessment system using human hepatocytes. However, human hepatocytes hardly form functional, extended bile canaliculi, a requirement for cholestasis risk assessment. We previously established a culture protocol for functional, extended bile canaliculi formation in human iPSC-derived hepatocytes. In this study, we modified this culture protocol to support the formation of functional, extended bile canaliculi in human cryopreserved hepatocytes (cryoheps). The production of bile acids, which induces bile canaliculi extension, increased time-dependently during bile canaliculi formation using this protocol, suggesting that increased bile acid production may be involved in the extended bile canaliculi formation. We have also shown that our culture protocol can be applied to cryoheps from multiple donors and that bile canaliculi can be formed stably among different culture batches. Furthermore, this protocol enables long-term maintenance of bile canaliculi and scaling down to culture in 96-well plates. We expect our culture protocol to be a breakthrough for in vitro cholestasis risk assessment.

Evaluation of Drug Blood-Brain-Barrier Permeability Using a Microfluidic Chip.

The blood-brain-barrier (BBB) is made up of blood vessels whose permeability enables the passage of some compounds. A predictive model of BBB permeability is important in the early stages of drug development. The predicted BBB permeabilities of drugs have been confirmed using a variety of in vitro methods to reduce the quantities of drug candidates needed in preclinical and clinical trials. Most prior studies have relied on animal or cell-culture models, which do not fully recapitulate the human BBB. The development of microfluidic models of human-derived BBB cells could address this issue. We analyzed a model for predicting BBB permeability using the Emulate BBB-on-a-chip machine. Ten compounds were evaluated, and their permeabilities were estimated. Our study demonstrated that the permeability trends of ten compounds in our microfluidic-based system resembled those observed in previous animal and cell-based experiments. Furthermore, we established a general correlation between the partition coefficient (Kp) and the apparent permeability (Papp). In conclusion, we introduced a new paradigm for predicting BBB permeability using microfluidic-based systems.