Stages Of Drug Development Research Articles

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting drug absorption can facilitate candidate screening and reduce time to market. Algorithms are available with good prediction accuracy that however focus only on solubility. In this work, we focused on drug permeability looking at human intestinal absorption as a marker for intestinal bioavailability. Being of considerable therapeutic relevance, APIs with serotonergic activity were selected as a dataset. Due to process complexity, experimental data scarcity, and variability, we turned toward an artificial intelligence (AI)-based system, which is a hierarchical combination of classification and regression models. This combination of seemingly two models into a single system widens the space of molecules classified as highly permeable with high accuracy. The specialized and optimized system enables in silico and structure-based prediction with a high degree of certainty. Predictions in external validation allowed correct selection of the 38% of highly permeable molecules without any false positives. The proposed system based on AI represents a promising tool useful for oral drug screening at an early stage of drug discovery and development. Datasets and the obtained models are available on the GitHub platform (https://github.com/nczub/HIA_5-HT).

Optimization of an α-aminonaphthylmethylphosphonic acid inhibitor of purple acid phosphatase using rational structure-based design approaches

Purple acid phosphatases (PAPs) are ubiquitous binuclear metallohydrolases that have been isolated from various animals, plants and some types of fungi. In humans and mice, elevated PAP activity in osteoclasts is associated with osteoporosis, making human PAP an attractive target for the development of anti-osteoporotic drugs. Based on previous studies focusing on phosphonate scaffolds, as well as a new crystal structure of a PAP in complex with a derivative of a previously synthesized α-aminonaphthylmethylphosphonic acid, phosphonates 24–40 were designed as new PAP inhibitor candidates. Subsequent docking studies predicted that all of these compounds are likely to interact strongly with the active site of human PAP and most are likely to interact strongly with the active site of pig PAP. The seventeen candidates were synthesized with good yields and nine of them (26–28, 30, 33–36 and 38) inhibit in the sub-micromolar to nanomolar range against pig PAP, with 28 and 35 being the most potent mammalian PAP inhibitors reported with Ki values of 168 nM and 186 nM, respectively. This study thus paves the way for the next stage of drug development for phosphonate inhibitors of PAPs as anti-osteoporotic agents.

Metabolic Perturbation Score-Based Mass Spectrometry Imaging Spatially Resolves a Functional Metabolic Response.

Metabolic perturbation score-based mass spectrometry imaging (MPS-MSI) is proposed to reveal the spatially resolved functional metabolic response associated with disease progression or drug action including metabolism pathways, species, biofunction, or biotransformation. The MPS-MSI enables the exploration of therapeutic or adverse effects, regional heterogeneous responses to drug treatment, possible molecular mechanisms, and even drug potential targets. MPS-MSI was demonstrated to be a promising molecular imaging tool not only for efficacy and safety evaluation but also for molecular mechanism investigation at the early stage of drug research and development.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a “toxicity index” for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

Clinical development time is shorter for new anticancer drugs approved via accelerated approval in the US or via conditional approval in the EU.

The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) offer expedited regulatory approval programs for drugs with high potential patient value applicable at different stages leading to marketing authorization: (i) drug development (fast track designation (FTD), breakthrough therapy designation (BTD), regenerative medicine advanced therapy designation in the United States, and priority medicines scheme in the European Union), (ii) review of marketing authorization application (priority review in the United States and accelerated assessment in the European Union), (iii) approval of drug (accelerated approval in the United States and conditional approval in the European Union). Typical clinical development time of 76 new anticancer drugs, for which the EMA gave a positive opinion between January 2010 and December 2019, was 6.7 years: 5.8 years for small molecules and 7.7 years for biotechnology-derived products. Drugs following only BTD (5.6 years) typically had a shorter clinical development time than drugs following only FTD (6.4 years) or both FTD and BTD (6.4 years), compared to drugs not following any expedited regulatory approval program at the drug development stage (7.7 years). Drugs following an expedited regulatory approval program at the stage of drug development and accelerated approval in the United States (FDA1 [4.5 years] and FDA3 [5.6 years]), and drugs following the standard procedure at the stage of drug development and conditional approval in the European Union (EMA5 [5.5 years] and EMA7 [4.5 years]) typically had a reduced clinical development time. These findings provide insight for the industry into combinations of expedited regulatory approval programs correlated with shorter clinical development time of new anticancer drugs.

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.

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.

Characterization and validation of a middle-down hydrophobic interaction chromatography method to monitor methionine oxidation in IgG1

Post-translational modifications (PTMs) of therapeutic monoclonal antibodies (mAbs) can impact the efficacy of a drug. Methionine oxidation can alter the overall hydrophobicity of an antibody, thereby inducing conformational changes and affecting its biological activity. To ensure high quality, safety, and efficacy of mAbs, routine monitoring of PTMs such as methionine (Met) oxidation is essential. Met oxidation in the fragment crystallizable (Fc) region of immunoglobulin-G1 (IgG1) is a potential critical quality attribute because it impacts not only the interaction with the neonatal Fc receptor and protein A but also the half-life of mAbs in serum circulation. Although bottom-up mass spectrometry provides high site specificity, it may have limited application in quality control workflows, and its complicated sample preparation could result in procedure-induced oxidation. In this study, we describe the development and characterization of a rapid and robust middle-down hydrophobic interaction chromatography method for monitoring Met oxidation in the Fc region of IgG1. Additionally, we assessed a comprehensive method validation package and demonstrated the specificity, linearity, precision, and accuracy of the new method within a range of 3.8–37.7%. The relative quantitative data provided by this method may be used in a regulated workflow to support process and formulation development as well as in the later stages of drug development and batch release and stability studies.

MvLognCorrEst: an R package for sampling from multivariate lognormal distributions and estimating correlations from uncomplete correlation matrix

Background and ObjectivePharmacometrics (PMX) is a quantitative discipline which supports decision-making processes in all stages of drug development. PMX leverages Modeling and Simulations (M&S), which represents a powerful tool to characterize and predict the behavior and the effect of a drug. M&S-based methods, such as Sensitivity Analysis (SA) and Global Sensitivity Analysis (GSA), are gaining interest in PMX as they allow the evaluation of model-informed inference quality. Simulations should be correctly designed to obtain reliable results. Neglecting correlations between model parameters can significantly alter the results of simulations. However, the introduction of a correlation structure between model parameters can cause some issues. Sampling from a multivariate lognormal distribution, which is the typically distribution assumed for PMX model parameters, is not straightforward when a correlation structure is introduced. Indeed, correlations need to respect some constraints which depend by the CVs (i.e., coefficients of variation) of lognormal variables. In addition, when correlation matrices have some unspecified values, they should be properly fixed preserving the positive semi-definiteness of the correlation structure. In this paper, we present mvLognCorrEst, an R package developed to address these issues. MethodsThe proposed sampling strategy was based on reconducting the extraction from the multivariate lognormal distribution of interest to the underlying Normal distribution. However, with high lognormal CVs, a positive semi-definite Normal covariance matrix cannot be obtained due to the violation of some theoretical constraints. In these cases, the Normal covariance matrix was approximated to its nearest positive definite matrix using Frobenius norm as matrix distance. For the estimation of unknown correlations terms, the graph theory was used to represent the correlation structure as weighed undirected graph. Plausible value ranges for the unspecified correlations were derived considering the paths between variables. Then, their estimation was performed by solving a constrained optimization problem. Results: Package functions are presented and applied on a real case study, that is the GSA of a PMX model that has been recently developed to support preclinical oncological studies. ConclusionsmvLognCorrEst package is an R tool to support simulation-based analysis for which sampling from multivariate lognormal distributions with correlated variables and/or estimation of partially defined correlation matrix are required.

β-Cyclodextrin and Folic Acid Host-Guest Interaction Binding Parameters Determined by Taylor Dispersion Analysis and Affinity Capillary Electrophoresis.

The thermodynamic properties of molecular recognition in host-guest inclusion complexes can be studied by Taylor dispersion analysis (TDA). Host-guest inclusion complexes have modest size, and it is possible to get convergent results fast, achieving greater certainty for the obtained thermodynamic properties. Cyclodextrins (CDs) and their derivatives can be used as drug carriers that can boost stability, solubility, and bioavailability of physiologically active substances. A simple and effective approach for assessing the binding properties of CD complexesthat are critical in the early stages of drug and formulation development is needed to fully understand the process of CD and guest molecules' complex formation. In this work, TDA was successfully used to rapidly determine interaction parameters, including binding constant and stoichiometry, between β-CD and folic acid (FA) along with the diffusivities of the free FA and its complex with β-CD. Additionally, the FA diffusion coefficient obtained by TDA was compared to the results previously obtained by NMR. Affinity capillary electrophoresis (ACE) was also used to compare the binding constants obtained by different methods. The results showed that the binding constants obtained by ACE were somewhat lower than those obtained by the two TDA procedures. This article is protected by copyright. All rights reserved.

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.

Drug–target interaction prediction based on protein features, using wrapper feature selection

Drug–target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug–target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.

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.

Review: Formulation Development Studies

Abstract: The formulation development is crucial component of pharmaceutical development and important for therapeutic and commercial acquirement of product by delivering quality, safety and efficiency. The main parts of formulation development are product development methods like drug discovery, research etc. It is an essential factor not only in the starting stage of drug development, but also in later marketing success of drug product. It ties the exploration of a new drug substance to the successful development of a drug product. Formulation can determine patentability, lifecycle the success of a pharmaceutical product.

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.

Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development

Drug discovery has traditionally been a time consuming and expensive endeavor. Additionally, drugs weren’t as effectively designed as those that are being predicted and developed through AI and ML today. Machine learning is a form of artificial intelligence that develops and evolves based on experience (similarly to the human mind), and is more recently being utilized in drug discovery and design. The integration of AI and ML into the drug discovery and development process has allowed for higher target precision, lower toxicity, and better dosage formulations. AI more generally has been introduced to and has been leveraged at, each step of drug development, including target identification and validation, hit identification, as well as hit to lead optimization, and has been key in shortening the previously lengthy drug screening process. AI and ML has also been applied downstream in drug formulation where it has maximized resource utilization and is allowing for web-based 3D printing of drugs. Application of AI in the drug development process has also been extended to the modeling of novel drug-like compounds to predict their ADMET properties. This review will address the stages of drug discovery and development in which the application of AI and ML modeling has altered the traditional development of drugs.
 Keywords: Drug discovery, machine learning, artificial intelligence, computational 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.

Drugs: Stages of drug development and methods to control the high usage of drugs

Drug development is a complex and lengthy process that involves several stages, from the discovery of a potential drug molecule to its approval for marketing and distribution to patients. The drug development process typically takes several years and involves extensive testing and evaluation to ensure the safety and efficacy of the drug. All the possible effects based on drugs, such as addiction, drug abuse, alcohol use, drug development, drug discovery, and the use of drugs by students, are studied in this paper. This paper discusses how the rating of drug use changes over time and how to control it. This crucial information has been presented in this paper to regulate individual's use of drugs and reduce the world's susceptibility to drugs.

Concepts and applications of pharmacokinetic parameters: A guide for the classroom

Context: Pharmacokinetic studies play a fundamental role in making informed decisions during the drug development stage and fulfilling regulatory agencies' requirements for drug approval. Disposition profiles of plasma drug concentrations over time can be characterized by using non-compartmental analysis, compartmental and physiological-based modeling. These models allow us to determine the pharmacokinetic parameters that best describe the absorption, distribution, metabolism, and excretion (ADME) processes. Aims: To develop a conceptual and practical guide for the classroom on the most relevant pharmacokinetic parameters and their applications. Results: The apparent volume of distribution (Vd), systemic drug clearance (CL), bioavailability (F) and elimination half-life (t1/2) are among the most relevant pharmacokinetic parameters discussed in this article. The Vd describes the relationship at equilibrium between the amount of drug in the body and its plasma concentrations after distribution, used to calculate the initial dose to reach the target drug concentration. The CL describes the relationship between plasma drug concentrations and the rate of elimination from the body, allowing calculation of a maintenance dosing rate to maintain an average target concentration at steady-state. The t1/2 is the time required to halve the plasma drug concentration, whereas F is critical to understand the biological performance of the drug formulation. Conclusions: In this teacher's topic text, we emphasize the importance of these parameters for optimizing strategies of model-informed dose individualization. Indeed, they are crucial for predicting systemic drug exposures and how long the drug will last in the body, as well as time to steady state after multiple-dosing regimens.

Evaluation strategy of drug permeability in new drug discovery and development stage

Drug permeability is an important factor determining the effectiveness and safety of drug treatment. In the early stage of new drug research and development, testing drug permeability can improve research and development efficiency and reduce research and development costs. Because of its advantages such as rapidity and simplicity, drug permeability has gradually attracted attention. This paper introduces the methods of two-phase distribution, chromatography, capillary electrophoresis, optical sensor and parallel artificial membrane permeation test at home and abroad in recent years.