Early Stages Of Drug Development Research Articles

Abstract 1535: A novel ELISA Kit:highly accuracy and sensitivity for GDF-15 detection

Abstract Growth/differentiation factor 15 (GDF-15) is a promising drug target and biomarker for cancer early screening or immunotherapy. Under normal conditions, GDF-15 is expressed in low concentrations in most organs. Increased protein levels are associated with disease states such as tissue hypoxia, inflammation, acute injury and oxidative stress. The development of GDF-15 detection kit can help pharmaceutical companies in the early stage of drug development. It can also facilitate the early screening of related diseases. Hence, we developed a novel GDF-15 detected ELISA kit based on double-antibody sandwich ELISA. Our kit has ultra-high sensitivity and specificity and the concentration of GDF-15 can be detected accurately in complex biological samples such as serum, plasma or urine. First of all, mice were immunized by GDF-15. After detecting the antibody titers in sera of the immunized mice, hybridoma technologies were used to produce GDF-15 monoclonal antibodies. 57 antibodies were analyzed by epitope competition, ELISA binding and KD binding detection. The specificity of pairwise candidates was screened to obtain optimal coating and detecting antibody pair. The detecting antibody was labeled with biotin which can be conjugated with streptavidin labeled HRP. Biotin-streptavidin labeling detection method can achieve signal amplification which offers an improved detection sensitivity. Our GDF-15 detection kit has better property in sensitivity and specificity. The detection range is from 10 pg/mL to 10000 pg/mL. LOD is 5 pg/mL and LOQ is 10 pg/mL. These parameters are better than many commercial test kits. Moreover, the concentration of GDF-15 can be detected accurately in complex clinical samples. We anticipate that this GDF-15 detection kit can be applied not only for companion diagnosis, but also for related drug development. Citation Format: Meng Liang, Feng He, Meng Zhang, Yinsong Ye, Shukun Chen, Jinying Ning, Feng Hao. A novel ELISA Kit:highly accuracy and sensitivity for GDF-15 detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1535.

Abstract 971: Thymidine Kinase activity - A liquid biomarker reflecting proliferation rate and treatment efficacy in solid tumors provides unique guidance in oncology trials and drug development

Abstract Thymidine Kinase (TK) plays a fundamental role in DNA synthesis and cell proliferation. Expression of the enzyme is highly cell cycle dependent and correlates with cell growth state. TK activity (TKa) is present in all proliferating cells but release into blood circulation is low or absent from quiescent cells. Numerous studies have shown TKa to be elevated in solid tumors as well as in the blood of cancer patients with active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, can measure and quantify TKa in serum or plasma isolated from human and animal blood samples. TKa as a biomarker offers valuable information at all stages of drug development; from drug efficacy and dose response studies in cell culture and tumor xenograft models, to patient selection and treatment monitoring in clinical trials. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and the minimally effective dose of a new compound rather than a maximally tolerated dose. In both cultured cells and mouse xenografts, a complete shutdown of tumor cell proliferation (assessed by TKa) can be observed well before and at much lower doses than when cell death or toxicity is used as the dose determination endpoint. The TKa-biomarker can also be used as a pharmacodynamic tool in clinical trials to improve study endpoints. Patient baseline levels (BL) of TKa are predictive/prognostic and can be used to identify which patients will respond to the therapy being tested vs those who will not. In metastatic breast cancer, a BL TKa value of <250 DuA is significantly correlated with longer PFS (HR=1.76, p<0.0001) and OS (HR=2.38, p<0.0001) independent of patient and disease characteristics. On-therapy TKa levels and dynamics can be used in clinical trials to evaluate response to a drug as early as two weeks into the first cycle. In patients receiving CDK4/6 inhibitors, TKa levels >140 DuA at cycle 1 day 15 predicts for lack of benefit to the drug (HR=5.65, p<0.0001). TKa can also be used as a predictive biomarker for drugs that cause an immune reaction and increase in cell proliferation such as immune checkpoint inhibitors (ICIs). When effective, ICIs cause an increase in immune cell proliferation which can be detected by elevated levels of TKa. Patients who do not achieve >1.5 fold change in TKa levels as compared to BL, have a significantly shorter duration of response to an ICI (HR=3.70, p=0.002) and patients with high TKa during ICI treatment have significantly longer OS (HR=3.49, p<0.05). In summary, TKa is a translational liquid biomarker that can bridge between preclinical and clinical studies, providing fundamental information for decision making during early and late stage drug development. Data and examples of the utility of TKa as a companion diagnostic for drug development will be presented. Citation Format: Mattias K. Bergqvist, Hanna Ritzén, Amy Williams. Thymidine Kinase activity - A liquid biomarker reflecting proliferation rate and treatment efficacy in solid tumors provides unique guidance in oncology trials and drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 971.

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development.

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor's own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.

Ensemble of structure and ligand-based classification models for hERG liability profiling.

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Biohybrid 3D Printing of a Tissue-Sensor Platform for Wireless, Real-Time, and Continuous Monitoring of Drug-Induced Cardiotoxicity.

Drug-induced cardiotoxicity is regarded as a major hurdle in the early stages of drug development. Although there are various methods for preclinical cardiotoxicity tests, they cannot completely predict the cardiotoxic potential of a compound due to the lack of physiological relevance. Recently, 3D engineered heart tissue (EHT) has been used to investigate cardiac muscle functions as well as pharmacological effects by exhibiting physiological auxotonic contractions. However, there is still no adequate platform for continuous monitoring to test acute and chronic pharmacological effects in vitro. Here, a biohybrid 3D printing method for fabricating a tissue-sensor platform, composed of a bipillar-grafted strain gauge sensor and EHT, is first introduced. Two pillars are three-dimensionally printed as grafts onto a strain gauge-embedded substrate to promote the EHT contractility and guide the self-assembly of the EHTs along with the strain gauge. In addition, the integration of a wireless multi-channel electronic system allows for continuous monitoring of the EHT contractile force by the tissue-sensor platform and, ultimately, for the observation of the acute and chronic drug effects of cardiotoxicants. In summary, biohybrid 3D printing technology is expected to be a potential fabrication method to provide a next-generation tissue-sensor platform for an effective drug development process.

Development of machine learning models for prediction of antibody non-specificity.

For the development of therapeutic antibodies, it is acknowledged that optimal target binding affinity should be accompanied by high specificity. In recent years, there has been an increased focus on lowering non-specific binding during early-stage drug development. In particular, non-specificity has been recognised as a root-cause for failure in numerous antibody-based drug programs due to unexpected pharmacokinetics and elevated toxicity. Hence, there is a need for predictive tools for flagging and deselecting non-specific antibodies at an early stage to mitigate failure at clinical late-stage development. From a computational design perspective, prediction has remained a challenge as non-specificity is currently a poorly understood biophysical phenomena, typically involving weak interactions to various, unrelated targets and surfaces. In this work, we describe the development of sequence-based machine learning (ML) models for prediction of antibody non-specificity. A dataset of >1000 antibody sequences paired with their respective non-specificity flag, defined by ELISA using a panel of common antigens, were retrieved from public sources (Jain et al., 2017; Boughter et al., 2020). The sequences were embedded using state-of-the-art protein language models for representation of their physiochemical properties, whereafter the vectorized embeddings were served as features for training of binary classifiers for non-specificity flags. A total of 12 different antibody domain-specific models were generated. Highest predictability of non-specificity was obtained for ML algorithms trained on the heavy-chain variable domain (VH), and the complementarity determining regions (CDRs), and resulted in a mean accuracy of 73% across 3-, 5-, and 10-fold cross-validations. The classification probability of non-specificity followed an uptrend with the experimental data (non-specificity flags), thereby mimicking regression behaviour. Altogether, our work demonstrates feasibility for prediction of non-specific binding from the antibody sequence, and that the non-specificity primarily stems from the VH domain, with main contributions from the heavy-chain CDRs.

In vitro co-culture systems of hepatic and intestinal cells for cellular pharmacokinetic and pharmacodynamic studies of capecitabine against colorectal cancer

BackgroundAs a prodrug of 5-fluorouracil (5-FU), orally administrated capecitabine (CAP) undergoes preliminary conversion into active metabolites in the liver and then releases 5-FU in the gut to exert the anti-tumor activity. Since metabolic changes of CAP play a key role in its activation, a single kind of intestinal or hepatic cell can never be used in vitro to evaluate the pharmacokinetics (PK) and pharmacodynamics (PD) nature. Hence, we aimed to establish a novel in vitro system to effectively assess the PK and PD of these kinds of prodrugs.MethodsCo-culture cellular models were established by simultaneously using colorectal cancer (CRC) and hepatocarcinoma cell lines in one system. Cell Counting Kit-8 (CCK-8) and flow cytometric analysis were used to evaluate cell viability and apoptosis, respectively. Apoptosis-related protein expression levels were measured using western blot analysis. A selective liquid chromatography-tandem mass spectrometry (LC–MS/MS) method was developed for cellular PK in co-culture models.ResultsCAP had little anti-proliferative effect on the five monolayer CRC cell lines (SW480, LoVo, HCT-8, HCT-116 and SW620) or the hepatocarcinoma cell line (HepG2). However, CAP exerted marked anti-tumor activities on each of the CRC cell lines in the co-culture models containing both CRC and hepatocarcinoma cell lines, although its effect on the five CRC cell lines varied. Moreover, after pre-incubation of CAP with HepG2 cells, the culture media containing the active metabolites of CAP also showed an anti-tumor effect on the five CRC cell lines, indicating the crucial role of hepatic cells in the activation of CAP.ConclusionThe simple and cost‑effective co-culture models with both CRC and hepatocarcinoma cells could mimic the in vivo process of a prodrug dependent on metabolic conversion to active metabolites in the liver, providing a valuable strategy for evaluating the PK and PD characteristics of CAP-like prodrugs in vitro at the early stage of drug development.

Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development.

Drug-induced liver injury (DILI) is a major factor influencing new drug withdrawal; therefore, an appropriate toxicity assessment at the preclinical stage is required. Previous in silico models have been established using compound information listed in large data sources, thereby limiting the DILI risk prediction for new drugs. Herein, we first constructed a model to predict DILI risk based on a molecular initiating event (MIE) predicted by quantitative structure-activity relationships, admetSAR parameters (e.g. cytochrome P450 reactivity, plasma protein binding, and water-solubility), and clinical information (maximum daily dose [MDD] and reactive metabolite [RM]) for 186 compounds. The accuracy of the models using MIE, MDD, RM, and admetSAR alone were 43.2%, 47.3%, 77.0%, and 68.9%, while the "predicted MIE+admetSAR + MDD+RM" model's accuracy was 75.7%. The contribution of MIE to the overall prediction accuracy was little effect or rather worsening it. However, it was considered that MIE was a valuable parameter and that it contributed to detect high DILI risk compounds in the early development stage. We next examined the effect of stepwise changes in MDD on altering the DILI risk and estimating the maximum safety dose (MSD) for clinical use based on structural information, admetSAR, and MIE parameters because it is important to estimate the dose that could prevent the DILI onset in clinical conditions. Low-MSD compounds might increase the DILI risk, as these compounds were classified as "most-DILI concern" at low doses. In conclusion, MIE parameters were especially useful to check the DILI concern compounds and to prevent the underestimation of DILI risk in the early stage of drug development.

Emerging Role of Biopharmaceutical Classification and Biopharmaceutical Drug Disposition System in Dosage form Development: A Systematic Review

Biopharmaceutical classification system (BCS) is an advanced tool used for classifying medicines based on dissolution, water solubility, and intestinal permeability, which affect the absorption of active pharmaceutical ingredients (API) from immediate-release solid oral forms. It is useful to the formulation researchers to develop novel dosage forms based on modernistic rather than experimental approaches. The current review focuses on the fundamentals, objectives, guidance of BCS, characteristics of BCS drugs, their importance and applications of BCS. This review explains the challenges in drug development in terms of solubility and in vivo disposition. In the current review, new strategies for improving BCS II drug solubility as well as biopharmaceutical drug disposition properties which are utilized throughout the early stages of drug development and commercialization are mainly discussed.

Hyphenated liquid chromatography - diode array detection - charged aerosol detection - high resolution - multistage mass spectrometry with online hydrogen/deuterium exchange: One stop solution for pharmaceutical impurity profiling

Hyphenation of different analytical techniques has always been advantageous in structural characterization as it saves time, money and resources. In the pharmaceutical sector, chromatography-based impurity profiling, including identification, characterization, and quantification in drug substances or finished products, is of utmost importance to comply with quality, patient safety and regulatory requirements. These impurities are monitored using LC-UV/DAD and identified and/or characterized using HRMS and MS/MS. LC analysis usually yields the area percent purity of the targeted peak, however, this is not sufficient for pharmaceutical purposes; where the regulatory requirement is to report impurities in percent weight by weight. Unfortunately, the non-availability of impurity standards and relative response factors at an early stage of drug development, risks the product quality due to the inability of the method to differentiate percent purity, and percent weight by weight. Hence, there is a need for a distinctive way of determining the relative response factor. In the current study, a unique hyphenation has been employed by integrating LC with DAD, CAD, and HRMSn with hydrogen-deuterium exchange. The LC flow, post-DAD detection has been diverted to CAD with an inverse gradient for relative response factor determination and MS Orbitrap for exact mass, and MSn fragmentation. A separate infusion pump has been incorporated to infuse D2O on a need basis, which can perform partial hydrogen deuterium exchange for determining the number of labile hydrogens in the impurity structure. This hyphenation has been validated with four model compounds and a total of nineteen chromatographic peaks. The technique provides ample information for their qualitative analysis along with percent weight-by-weight values, which fulfils the regulatory requirements and can be used as one-stop solution for impurity profiling.

Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer in women. It has the poorest prognosis along with limited therapeutic options. Smart nano-based carriers are emerging as promising approaches in treating TNBC due to their favourable characteristics such as specifically delivering different cargos to cancer cells. However, nanoparticles' tumour cell uptake, and subsequent drug release, are essential factors considered during the drug development process. Contemporary qualitative analyses based on imaging are cumbersome and prone to human biases. Deep learning-based algorithms have been well-established in various healthcare settings with promising scope in drug discovery and development. In this study, the performance of five different convolutional neural network models was evaluated. In this research, we investigated two sequential models from scratch and three pre-trained models, VGG16, ResNet50, and Inception V3. These models were trained using confocal images of nanoparticle-treated cells loaded with a fluorescent anticancer agent. Comparative and cross-validation analyses were further conducted across all models to obtain more meaningful results. Our models showed high accuracy in predicting either high or low drug uptake and release into TNBC cells, indicating great translational potential into practice to aid in determining cellular uptake at the early stages of drug development in any area of research.

Development and validation of a porcine artificial colonic mucus model reflecting the properties of native colonic mucus in pigs

Colonic mucus plays a key role in colonic drug absorption. Mucus permeation assays could therefore provide useful insights and support rational formulation development in the early stages of drug development. However, the collection of native colonic mucus from animal sources is labor-intensive, does not yield amounts that allow for routine experimentation, and raises ethical concerns. In the present study, we developed an in vitro porcine artificial colonic mucus model based on the characterization of native colonic mucus. The structural properties of the artificial colonic mucus were validated against the native secretion for their ability to capture key diffusion patterns of macromolecules in native mucus. Moreover, the artificial colonic mucus could be stored under common laboratory conditions, without compromising its barrier properties. In conclusion, the porcine artificial colonic mucus model can be considered a biorelevant way to study the diffusion behavior of drug candidates in colonic mucus. It is a cost-efficient screening tool easily incorporated into the early stages of drug development and it contributes to the implementation of the 3Rs (refinement, reduction, and replacement of animals) in the drug development process.

Establishment and validation of a torsade de pointes prediction model based on human iPSC‑derived cardiomyocytes.

Drug-induced cardiotoxicity is one of the main causes of drug failure, which leads to subsequent withdrawal from pharmaceutical development. Therefore, identifying the potential toxic candidate in the early stages of drug development is important. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a useful tool for assessing candidate compounds for arrhythmias. However, a suitable model using hiPSC-CMs to predict the risk of torsade de pointes (TdP) has not been fully established. The present study aimed to establish a predictive TdP model based on hiPSC-CMs. In the current study, 28 compounds recommended by the Comprehensive in vitro Proarrhythmia Assay (CiPA) were used as training set and models were established in different risk groups, high- and intermediate-risk versus low-risk groups. Subsequently, six endpoints of electrophysiological responses were used as potential model predictors. Accuracy, sensitivity and area under the curve (AUC) were used as evaluation indices of the models and seven compounds with known TdP risk were used to verify model differentiation and calibration. The results showed that among the seven models, the AUC of logistic regression and AdaBoost model was higher and had little difference in both training and test sets, which indicated that the discriminative ability and model stability was good and excellent, respectively. Therefore, these two models were taken as submodels, similar weight was configured and a new TdP risk prediction model was constructed using a soft voting strategy. The classification accuracy, sensitivity and AUC of the new model were 0.93, 0.95 and 0.92 on the training set, respectively and all 1.00 on the test set, which indicated good discrimination ability on both training and test sets. The risk threshold was defined as 0.50 and the consistency between the predicted and observed results were 92.8 and 100% on the training and test sets, respectively. Overall, the present study established a risk prediction model for TdP based on hiPSC-CMs which could be an effective predictive tool for compound-induced arrhythmias.

3D printing: Innovative solutions for patients and pharmaceutical industry

Three-dimensional (3D) printing is an emerging technology with great potential in pharmaceutical applications, providing innovative solutions for both patients and pharmaceutical industry. This technology offers precise construction of the structure of dosage forms and can benefit drug product design by providing versatile release modes to meet clinical needs and facilitating patient-centric treatment, such as personalized dosing, accommodate treatment of specific disease states or patient populations. Utilization of 3D printing also facilitates digital drug product development and manufacturing. Development of 3D printing at early clinical stages and commercial scale pharmaceutical manufacturing has substantially advanced in recent years. In this review, we discuss how 3D printing accelerates early-stage drug development, including pre-clinical research and early phase human studies, and facilitates late-stage product manufacturing as well as how the technology can benefit patients. The advantages, current status, and challenges of employing 3D printing in large scale manufacturing and personalized dosing are introduced respectively. The considerations and efforts of regulatory agencies to address 3D printing technology are also discussed.

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

Some considerations on target estimands for health technology assessment.

Some considerations on target estimands for health technology assessment.

Cocrystal virtual screening based on the XGBoost machine learning model

Co-crystal formation can improve the physicochemical properties of a compound, thus enhancing its druggability. Therefore, artificial intelligence-based co-crystal virtual screening in the early stage of drug development has attracted extensive attention from researchers. However, the complexity of developing and applying algorithms hinders it wide application. This study presents a data-driven co-crystal prediction method based on the XGBoost machine learning model of the scikit-learn package. The simplified molecular input line entry specification (SMILES) information of two compounds is simply inputted to determine whether a co-crystal can be formed. The data set includs the co-crystal records presented in the Cambridge Structural Database (CSD) and the records of no co-crystal formation from extant literature and experiments. RDKit molecular descriptors are adopted as the features of a compound in the data set. The developed model shows excellent performance in the proposed co-crystal training and validation sets with high accuracy, sensitivity, and F1 score. The prediction success rate of the model exceeds 90%. The model therefore provides a simple and feasible scheme for designing and screening co-crystal drugs efficiently and accurately.

Characterization of KLH-driven immune responses in clinical studies: A systematic review

The pharmacological activity assessment of novel immunomodulatory drugs in early-stage drug development is challenging as healthy volunteers do not express relevant immune biomarkers. Alternatively, the immune system can be challenged with keyhole limpet hemocyanin (KLH), a suitable antigen for studying adaptive immune responses. This report systemically reviews the KLH challenge in clinical studies focusing on the characterization of the KLH-driven systemic and local immune responses, identification of the KLH-induced biomarkers, and the evaluation of the effect of pharmacological interventions and diseases on the KLH response. A systematic literature review was carried out in PubMed spanning from 1967 to 2022. The systemic humoral KLH responses could be characterized by ELISA after 3 weeks following immunization. For the systemic cellular and molecular immune responses multiple KLH immunizations and the use of novel techniques such as flow cytometry and ELISpot yield optimal results. The objective evaluation of dermal KLH rechallenge allows for more accurate and sensitive quantification of the local response compared to subjective scoring. For the local cellular and molecular assays after KLH dermal rechallenge we also advocate the use of multiple KLH immunizations. Furthermore, oral KLH feeding, age, physical activity, alcohol consumption, stress, as well as certain auto-immune diseases also play a role in the KLH-induced immune response. Importantly, based on the KLH challenges, the effect of (novel) immunomodulatory drugs could be demonstrated in healthy volunteers, providing valuable information for the clinical development of these compounds. This review underlines the value of KLH challenges in clinical studies, but also the need for standardized and well-controlled methodology to induce and evaluate KLH responses.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifierCRD42022335419

Cognitive Impairment Associated with Schizophrenia: From Pathophysiology to Treatment.

Cognitive impairment is a core feature of schizophrenia and a major contributor to poor functional outcomes. Methods for assessment of cognitive dysfunction in schizophrenia are now well established. In addition, there has been increasing appreciation in recent years of the additional role of social cognitive impairment in driving functional outcomes and of the contributions of sensory-level dysfunction to higher-order impairments. At the neurochemical level, acute administration of N-methyl-d-aspartate receptor (NMDAR) antagonists reproduces the pattern of neurocognitive dysfunction associated with schizophrenia, encouraging the development of treatments targeted at both NMDAR and its interactome. At the local-circuit level, an auditory neurophysiological measure, mismatch negativity, has emerged both as a veridical index of NMDAR dysfunction and excitatory/inhibitory imbalance in schizophrenia and as a critical biomarker for early-stage translational drug development. Although no compounds have yet been approved for treatment of cognitive impairment associated with schizophrenia, several candidates are showing promise in early-phase testing.

Construction of a culture protocol for functional bile canaliculi formation to apply human iPS cell-derived hepatocytes for cholestasis evaluation

Cholestatic toxicity causes the failure of pharmaceutical agents during drug development and, thus, should be identified at an early stage of drug discovery and development. The formation of functional bile canaliculi in human hepatocytes is required for in vitro cholestasis toxicity tests conducted during the early stage of drug development. In this study, we investigated the culture conditions required for the formation of bile canaliculi using human-induced pluripotent stem cell-derived hepatocytes (hiPSC-Heps). When hiPSC-Heps were sandwich-cultured under the condition we established, extended bile canaliculi were formed on the whole well surfaces. Biliary efflux transporters were localized in the formed bile canaliculi structures which had junctional complexes. After the model substrates of the biliary efflux transporters were taken up into cells, their subsequent excretion into the bile canaliculi was observed and was found to be impeded by each inhibitor of the biliary efflux transporter. These findings suggest that bile canaliculi have transporter-specific bile excretion abilities. We will continue to study the application of this culture protocol to cell-based cholestasis assay system. As a result, the culture protocol could lead to a highly predictable, robust cell-based cholestasis assay system because it forms functional bile canaliculi reproducibly and efficiently.