Early Stages Of Drug Development Research Articles

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 co-administered 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 co-administered 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 co-administration of 7 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 two-fold 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 DDIs caused by CYP3A induction was investigated. The predictability was verified using 7 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.

Insights into Early Interactions on Innovative Developments with European Regulators.

The European Medicines Agency Innovation Task Force (ITF) acts as early point of contact for medicine and technology developers to enable innovation during early drug development stages through ITF briefing meetings. To reflect on the current pace of innovation and to assess the potential of ITF stakeholder interactions, a comprehensive analysis of the ITF briefing meetings held between 2021 and 2022 was conducted with a focus on individual questions raised by the developers and the related feedback provided by the European regulators. Questions raised during ITF briefing meetings were extracted and categorised into main and sub-categories, revealing different themes across the whole medicine development process such as manufacturing technologies, pre-clinical developments, and clinically relevant questions. There was positive feedback from regulators who gave initial guidance in 85% of the answers, provided concrete examples in 20% of the answers and recommended to continue discussions through additional regulatory procedures in 22% of the answers. This analysis frames the content and the type of topics discussed during ITF briefing meetings. Moreover, it describes the type of regulatory feedback provided to medicine developers and identified potential for improvement of these early interactions. Therefore, this analysis emphasises the role of ITF briefing meetings in fostering innovation in medicine.

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.

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

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.

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.