Early Stages Of Drug Development Research Articles

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.

Equivariant 3D-conditional diffusion model for molecular linker design

AbstractFragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant three-dimensional conditional diffusion model for molecular linker design. Given a set of disconnected fragments, our model places missing atoms in between and designs a molecule incorporating all the initial fragments. Unlike previous approaches that are only able to connect pairs of molecular fragments, our method can link an arbitrary number of fragments. Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments. We demonstrate that DiffLinker outperforms other methods on the standard datasets, generating more diverse and synthetically accessible molecules. We experimentally test our method in real-world applications, showing that it can successfully generate valid linkers conditioned on target protein pockets.

Emerging In Vitro and In Vivo Models: Hope for the Better Understanding of Cancer Progression and Treatment.

Various cancer models have been developed to aid the understanding of the underlying mechanisms of tumor development and evaluate the effectiveness of various anticancer drugs in preclinical studies. These models accurately reproduce the critical stages of tumor initiation and development to mimic the tumor microenvironment better. Using these models for target validation, tumor response evaluation, resistance modeling, and toxicity comprehension can significantly enhance the drug development process. Herein, various in vivo or animal models are presented, typically consisting of several mice and in vitro models ranging in complexity from transwell models to spheroids and CRISPR-Cas9 technologies. While in vitro models have been used for decades and dominate the early stages of drug development, they are still limited primary to simplistic tests based on testing on a single cell type cultivated in Petri dishes. Recent advancements in developing new cancer therapies necessitate the generation of complicated animal models that accurately mimic the tumor's complexity and microenvironment. Mice make effective tumor models as they are affordable, have a short reproductive cycle, exhibit rapid tumor growth, and are simple to manipulate genetically. Human cancer mouse models are crucial to understanding the neoplastic process and basic and clinical research improvements. The following review summarizes different in vitro and in vivo metastasis models, their advantages and disadvantages, and their ability to serve as a model for cancer research.

A novel biomarker assay qualification strategy for rare human matrices - a case study of biomarkers in aqueous humor.

Background: Measuring pharmacodynamic biomarkers near the therapeutic site of action presents considerable challenges for sites with limited matrix volume or difficult access. Bioanalytical method qualification requires theuse of numerous matrix samples, which is problematic for rare matrices. The aim of this study was to design and implement a streamlined, fit-for-purpose strategy for qualification of biomarker assays in rare matrices. Materials & methods: A multiplexed biomarker immunoassay was developed in human aqueous humor. Results: Our strategy was successfully implemented, providing characterization of assay performance while reducing number of samples in assay qualification. Our assay was used in clinical trial support for an ophthalmic drug candidate. Conclusion: Our results indicate this approach can be applied to other earlystage drug development programs facing similar challenges.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations.

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.

In silico off-target profiling for enhanced drug safety assessment

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Improved seizure liability detection by combining rat hippocampal brain slice electrophysiology with in vivo behavior observation following intracerebroventricular drug administration

An adverse effect of drug candidates, seizure is a serious issue in drug development. Improving evaluation systems for seizure liability is crucial for selecting good candidates. Firstly, in vitro electrophysiological measurement by a multielectrode array system in rat hippocampal brain slices was employed to confirm an increase in electrically evoked population spike (PS) area, the occurrence of multiple population spikes (MPSs), and thereby the seizure liability of five positive control chemicals: picrotoxin, 4-aminopyridine, pentylenetetrazole, penicillin G, and chlorpromazine. Aspirin, a negative control, did not affect PS area or generate MPSs. Furthermore, baclofen, an anticonvulsant drug, decreased PS area and inhibited the increase in PS area or occurrence of MPSs induced by picrotoxin. A comparative study of seizure liability among carbapenem antibiotics revealed that tienam > carbenin > omegacin and finibax. Despite leading to a strong decrease in PS area, physostigmine, cisplatin, and paroxetine still produced MPSs. Therefore, the increase in PS area or the occurrence of the MPS are considered significant evaluation parameters for seizure liability.In contrast, the in vitro electrophysiological measurement could not detect the seizure liability of diphenhydramine or fluvoxamine. A follow-up study of in vivo mouse behavioral change induced by intracerebroventricular administration of these drugs clearly detected convulsions. The in vitro electrophysiological study using hippocampal brain slices combined with in vivo behavior observation study of drug candidates administered by intracerebroventricular injection can implement to assess the seizure liability of even small amounts, especially in the early stages of drug development.

Method development for 14C-labeling of IgG antibodies in preparation for clinical trials

Abstract Objectives Carbon-14 (14C) labeling is a standard technology for tracing molecules and providing their pharmacokinetic profiles. However, its primary focus has been on small molecules, with limited application to biomacromolecules. Particularly in the development of new biological entities (NBE), the utilization of microdosing with a 14C-labeled biomacromolecule proves beneficial in the early stages of drug development, contributing to significant time and cost savings. This study investigates the 14C-labeling of antibody and explores the stability of 14C-labeled antibody under various storage conditions. Methods and results In this study, the utilization of 14C-formaldehyde for labeling target antibodies at various molar ratios revealed a direct correlation between labeling efficiency and the quantity of 14C-formaldehyde applied: 1.5 mol/mol for 14C-labeled antibody with the use of 10 equivalents of 14C-formaldehyde, 3.8 mol/mol for 14C-labeled antibody with the use of 10 equivalents of 14C-formaldehyde, and 10.5 mol/mol for 14C-labeled antibody with the use of 60 equivalents of 14C-formaldehyde. All the reaction conditions exhibited no antibody degradation, as evidenced by the absence of a significant change in HPLC purity compared to the unlabeled antibody. Stability tests revealed that all groups maintained their purities over a 4-week period at both − 75 ± 10 °C and 5 ± 3 °C. Given safety concerns related to internal radiation exposure in potential human subjects during microdosing, this study established optimal conditions for employing 14C-labeled antibodies. Therefore, it is optimized that 10 equivalents of 14C-formaldehyde can be used for 14C-antibody labeling through reductive amination, storing the antibodies at 5 ± 3 °C, and assigning a storage period of 4 weeks. Conclusion The findings from this study offer valuable insights into the effective application of 14C-labeling in microdosing studies, especially for larger molecules such as antibodies.

A minimal physiologically based pharmacokinetic model to study the combined effect of antibody size, charge, and binding affinity to FcRn/antigen on antibody pharmacokinetics.

Protein therapeutics have revolutionized the treatment of a wide range of diseases. While they have distinct physicochemical characteristics that influence their absorption, distribution, metabolism, and excretion (ADME) properties, the relationship between the physicochemical properties and PK is still largely unknown. In this work we present a minimal physiologically-based pharmacokinetic (mPBPK) model that incorporates a multivariate quantitative relation between a therapeutic's physicochemical parameters and its corresponding ADME properties. The model's compound-specific input includes molecular weight, molecular size (Stoke's radius), molecular charge, binding affinity to FcRn, and specific antigen affinity. Through derived and fitted empirical relationships, the model demonstrates the effect of these compound-specific properties on antibody disposition in both plasma and peripheral tissues using observed PK data in mice and humans. The mPBPK model applies the two-pore hypothesis to predict size-based clearance and exposure of full-length antibodies (150kDa) and antibody fragments (50-100kDa) within a onefold error. We quantitatively relate antibody charge and PK parameters like uptake rate, non-specific binding affinity, and volume of distribution to capture the relatively faster clearance of positively charged mAb as compared to negatively charged mAb. The model predicts the terminal plasma clearance of slightly positively and negatively charged antibody in humans within a onefold error. The mPBPK model presented in this work can be used to predict the target-mediated disposition of a drug when compound-specific and target-specific properties are known. To our knowledge, a combined effect of antibody weight, size, charge, FcRn, and antigen has not been incorporated and studied in a single mPBPK model previously. By conclusively incorporating and relating a multitude of protein's physicochemical properties to observed PK, our mPBPK model aims to contribute as a platform approach in the early stages of drug development where many of these properties can be optimized to improve a molecule's PK and ultimately its efficacy.

13 Cβ-Valine and 13 Cγ-Leucine Methine Labeling To Probe Protein Ligand Interaction.

Precise information regarding the interaction between proteins and ligands at molecular resolution is crucial for effectively guiding the optimization process from initial hits to lead compounds in early stages of drug development. In this study, we introduce a novel aliphatic side chain isotope-labeling scheme to directly probe interactions between ligands and aliphatic sidechains using NMR techniques. To demonstrate the applicability of this method, we selected a set of Brd4-BD1 binders and analyzed 1 H chemical shift perturbation resulting from CH-π interaction of Hβ -Val and Hγ -Leu as CH donors with corresponding ligand aromatic moieties as π acceptors.

An Evolved Transformer Model for ADME/Tox Prediction

Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, artificial intelligence techniques such as Graphormer are showing increasing promise and usefulness to perform custom models for molecule modeling tasks. However, Graphormer and other transformer-based models do not consider the molecular fingerprint, as well as the physicochemicals that have been proved effective in traditional computational drug research. Here, we propose an enhanced model based on Graphormer which uses a tree model that fully integrates some known information and achieves better prediction and interpretability. More importantly, the model achieves new state-of-the-art results on ADME/Tox properties prediction benchmarks, surpassing several challenging models. Experimental results demonstrate an average SMAPE (Symmetric Mean Absolute Percentage Error) of 18.9 and a PCC (Pearson Correlation Coefficient) of 0.86 on ADME/Tox prediction test sets. These findings highlight the efficacy of our approach and its potential to enhance drug discovery processes. By leveraging the strengths of Graphormer and incorporating additional molecular descriptors, our model offers improved predictive capabilities, thus contributing to the advancement of ADME/Tox prediction in drug development. The integration of various information sources further enables better interpretability, aiding researchers in understanding the underlying factors influencing the predictions. Overall, our work demonstrates the potential of our enhanced model to expedite drug discovery, reduce costs, and enhance the success rate of our pharmaceutical development efforts.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

Ligand-Based Competition Binding by Real-Time 19F NMR in Human Cells.

The development of more effective drugs requires knowledge of their bioavailability and binding efficacy directly in the native cellular environment. In-cell nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for investigating ligand-target interactions directly in living cells. However, the target molecule may be NMR-invisible due to interactions with cellular components, while observing the ligand by 1H NMR is impractical due to the cellular background. Such limitations can be overcome by observing fluorinated ligands by 19F in-cell NMR as they bind to the intracellular target. Here we report a novel approach based on real-time in-cell 19F NMR that allows measuring ligand binding affinities in human cells by competition binding, using a fluorinated compound as a reference. The binding of a set of compounds toward Hsp90α was investigated. In principle, this approach could be applied to other pharmacologically relevant targets, thus aiding the design of more effective compounds in the early stages of drug development.

Development of a canine artificial colonic mucus model for drug diffusion studies

Colonic mucus is a key factor in the colonic environment because it may affect drug absorption. Due to the similarity of human and canine gastrointestinal physiology, dogs are an established preclinical species for the assessment of controlled release formulations. Here we report the development of an artificial colonic mucus model to mimic the native canine one. In vitro models of the canine colonic environment can provide insights for early stages of drug development and contribute to the implementation of the 3Rs (refinement, reduction, and replacement) of animal usage in the drug development process. Our artificial colonic mucus could predict diffusion trends observed in native mucus and was successfully implemented in microscopic and macroscopic assays to study macromolecular permeation through the mucus. The traditional Transwell set up was optimized with the addition of a nylon filter to ensure homogenous representation of the mucus barrier in vitro. In conclusion, the canine artificial colonic mucus can be used to study drug permeation across the mucus and its flexibility allows its use in various set ups depending on the nature of the compound under investigation and equipment availability.

DODII: Bayesian Dose Optimization Design for Randomized Phase II Trials

The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improved anti-cancer activity. With the initiation of ‘Project Optimus’ at the FDA, a randomized dose optimization study at the early stage of drug development has become necessary for oncology drug development. We propose a Bayesian Dose Optimization Design for Randomized Phase II trials (DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach in a randomized design, where efficacy and toxicity endpoints are jointly used to inform the dose selection. The adaptive feature of the DODII design will terminate a suboptimal dose at an interim analysis under two circumstances: 1) when efficacy or safety data is unpromising, 2) when the other dose option is clearly better. The critical values of the decision rule for the DODII design can be enumerated prior to the initiation of the clinical study, which makes it practically easy to implement. Through simulation studies, we showed that the DODII design has favorable operating characteristics with controlled selection error, higher power to select the optimal dose option, and reduced total sample size as compared to the other commonly used methods.

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example.

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Abstract A149: Characterization of HER3 targeted radioligand therapy using molecular imaging

Abstract Background: Radioligand therapy (RLT) is an emerging treatment modality that has shown potential to improve survival in cancer patients that often have limited or non-effective therapeutic options. We previously demonstrated that anti-HER3 antibody radioconjugate (HER3 ARC) has antitumor efficacy against HER3 expressing tumor xenograft models. One important step in the early development of RLT is to understand the targeting properties and the distribution of the RLT in vivo. Non-invasive molecular imaging such as single photon emission computed tomography (SPECT) and positron emission tomography (PET) offer unique opportunities to understand drug behavior in vivo, permitting real time drug targeting and distribution analysis. In this study, we assessed the targeting properties and distribution of our HER3 ARC in HER3 expressing preclinical xenograft tumor models using both SPECT and PET. Methods: HER3 monoclonal antibody was conjugated with p-SCN-Bn-deferoxamine (DFO) or p-SCN-Bn-DOTA (DOTA) then labeled with Zirconium-89 (89Zr) or Indium-111 (111In), respectively. Flow cytometry and gamma counting was used to characterize HER3 ARC biological activity in HER3-expressing tumor cell lines. Balb/c mice subcutaneously inoculated with HER3-expressing xenograft tumors (lung, ovarian and colorectal) were intravenously administered with 89Zr-DFO-HER3 or 111In-DOTA-HER3. PET or SPECT scans were acquired 72 and 120-144hrs post injection of the radiotracer. Separate cohorts of mice were simultaneously administered with 89Zr-DFO-HER3 or 111In-DOTA-HER3 and 20-fold excess of native HER3 antibody as control to block radiotracer binding. Tissue uptake was quantified by drawing 3D or 2D regions of interest (ROI) for PET or SPECT images, respectively. Results: HER3 ARC radiotracers were successfully prepared and demonstrated similar binding to HER3 expressing cells compared to native HER3 antibody. In tumor-bearing mice, 89Zr-DFO-HER3 and 111In-DOTA-HER3 specifically accumulated in HER3 expressing tumors and cleared throughout the reticuloendothelial system. High tumor uptake was observed 120hrs post radiotracer injection using both imaging modalities. In mice pre-treated with 20-fold excess of native antibody, tumor uptake was significantly lower, showing tumor binding specificity of ARC. PET and SPECT images showed similar tracer distribution profile in the HER3 tumors. As expected, radiotracer clearance organ signals were also visualized. Conclusions: In this study we show that PET or SPECT imaging using 89Zr or 111In, respectively, offers the potential to assess the distribution of HER3 targeted radioligand therapy during early stages of drug development. HER3 ARC showed highly specific HER3 tumor uptake that remained at least 120hrs post injection. Our findings warrant further investigation to support advancement of HER3 ARC as a therapeutic option. Citation Format: Sumit Mukherjee, Debbie Lewis, Jason Li, Le-Cun Xu, Amanda Chin, Mary Chen, Monideepa Roy, Patrik Brodin, William van der Touw, Helen Kotanides, Denis Beckford-Vera. Characterization of HER3 targeted radioligand therapy using molecular imaging [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A149.

EE225 Exploring the Value of Country-Specific Cost-Effectiveness Models in Early Stages of Drug Development

EE225 Exploring the Value of Country-Specific Cost-Effectiveness Models in Early Stages of Drug Development

Shifting the Focus from Dissolution to Permeation: Introducing the Meso-fluidic Chip for Permeability Assessment (MCPA)

In response to the growing ethical and environmental concerns associated with animal testing, numerous in vitro tools of varying complexity and biorelevance have been developed and adopted in pharmaceutical research and development. In this work, we present one of these tools, i.e., the Meso-fluidic Chip for Permeability Assessment (MCPA), for the first time. The MCPA combines an artificial barrier (PermeaPad®) with an organ-on-chip device (MIVO®) and real-time automated concentration measurements, to yield a sustainable, yet effortless method for permeation testing. The system offers three major physiological aspects, i.e., a biomimetic membrane, an optimal membrane interfacial area-to-donor-volume-ratio (A/V) and a physiological flow on the acceptor/basolateral side, which makes the MPCA an ideal candidate for mechanistic studies and excellent in vivo bioavailability predictions. We validated the method with a handful of assorted drug compounds in unstirred and stirred donor conditions, before exploring its applicability as a tool for dissolution/permeation testing on a BCS class III/I drug (pyrazinamide) crystalline adducts and BCS class II/IV (hydrocortisone) amorphous solid dispersions. The results were highly reproducible and clearly displayed the method's potential for evaluating the performance of enabling formulations, and possibly even predicting in vivo performance. We believe that, upon further development, the MCPA will serve as a useful in vitro tool that could push sustainability into pharmaceutics by refining, reducing and replacing animal testing in early-stage drug development.