MICRO-TAG enzyme complementation enables quantification of cellular drug-target engagement in temperature series.

Drug discovery and development is a time-consuming, complex, and expensive process. Usually, it takes about 15 years in the best scenario since drug candidates have a high attrition rate. Therefore, drug development projects rarely take place in low and middle-income countries (LMICs). Traditionally, this process consists of four sequential stages: (1) target identification and early drug discovery, (2) preclinical studies, (3) clinical development, and (4) review, approval and monitoring by regulatory agencies.During the last decades, computational tools have offered interesting opportunities for Research and Development (R & D) in LMICs, since these techniques are affordable, reduce wet lab experiments in the first steps of the drug discovery process, reduce animal testing by aiding experiment design, and also provide key knowledge involving clinical data management as well as statistical analysis. This book chapter aims to highlight different computational tools to enable early drug discovery and preclinical studies in LMICs for different pathologies, including cancer. Several strategies for drug target selection are discussed: identification, prioritization and validation of therapeutic targets; particularly focusing on high-throughput analysis of different “omics” approaches using publicly available data sets. Next, strategies to identify and optimize novel drug candidates as well as computational tools for costeffective drug repurposing are presented. In this stage, chemoinformatics is a key emerging technology. It is important to note that additional computational methods can be used to predict possible uses of identified human-aimed drugs for veterinary purposes. Application of computational tools is also possible for predicting pharmacokinetics and pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it has a profound impact on drug discovery success. Finally, artificial intelligence (AI) has also served as a potential tool for drug design and discovery, expected to be a revolution for drug development in several diseases.It is important to note that the development of drug discovery projects is feasible in LMICs and in silico tools are expected to potentiate novel therapeutic strategies in different diseases.This book chapter aims to highlight different computational tools to enable early drug discovery and preclinical studies in LMICs for different pathologies, including cancer. Several strategies for drug target selection are discussed: identification, prioritization and validation of therapeutic targets; particularly focusing on high-throughput analysis of different “omics” approaches using publicly available data sets. Next, strategies to identify and optimize novel drug candidates as well as computational tools for costeffective drug repurposing are presented. In this stage, chemoinformatics is a key emerging technology. It is important to note that additional computational methods can be used to predict possible uses of identified human-aimed drugs for veterinary purposes.Application of computational tools is also possible for predicting pharmacokinetics and pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it has a profound impact on drug discovery success. Finally, artificial intelligence (AI) has also served as a potential tool for drug design and discovery, expected to be a revolution for drug development in several diseases.Application of computational tools is also possible for predicting pharmacokinetics and pharmacodynamics as well as drug-drug interactions. Drug safety is a key issue and it has a profound impact on drug discovery success. Finally, artificial intelligence (AI) has also served as a potential tool for drug design and discovery, expected to be a revolution for drug development in several diseases.

Introduction: Drug discovery is a long and costly process. Innovations and paradigm shifts are essential for continuous improvement in the productivity of pharmaceutical R&D.Areas covered: The author reviews the progress of label-free cell phenotypic and computational approaches in early drug discovery since 2004 and proposes a novel paradigm, which combines both approaches.Expert opinion: Label-free cell phenotypic profiling techniques offer an unprecedented and integrated approach to comprehend drug–target interactions in their native environments. However, these approaches have disadvantages associated with the lack of molecular details. Computational approaches, including ligand-, structure- and phenotype-based virtual screens, have become versatile tools in the early drug discovery process. However, these approaches mostly predict the binding of drug molecules to targets of interest and are limited to targets that are either well annotated for ligands or that are structurally resolved. Thus, combining label-free cell phenotypic profiling with computational approaches can provide a potential paradigm to accelerate novel drug discovery by taking advantages of the best of both approaches.

A possible future for the pharmaceutical industry.

Cellular drug targets exist within networked function-generating systems whose constituent molecular species undergo dynamic interdependent non-equilibrium state transitions in response to specific perturbations (i.e.. inputs). Cellular phenotypic behaviors are manifested through the integrated behaviors of such networks. However, in vitro data are frequently measured and/or interpreted with empirical equilibrium or steady state models (e.g. Hill, Michaelis-Menten, Briggs-Haldane) relevant to isolated target populations. We propose that cells act as analog computers, "solving" sets of coupled "molecular differential equations" (i.e. represented by populations of interacting species)via "integration" of the dynamic state probability distributions among those populations. Disconnects between biochemical and functional/phenotypic assays (cellular/in vivo) may arise with targetcontaining systems that operate far from equilibrium, and/or when coupled contributions (including target-cognate partner binding and drug pharmacokinetics) are neglected in the analysis of biochemical results. The transformation of drug discovery from a trial-and-error endeavor to one based on reliable design criteria depends on improved understanding of the dynamic mechanisms powering cellular function/dysfunction at the systems level. Here, we address the general mechanisms of molecular and cellular function and pharmacological modulation thereof. We outline a first principles theory on the mechanisms by which free energy is stored and transduced into biological function, and by which biological function is modulated by drug-target binding. We propose that cellular function depends on dynamic counter-balanced molecular systems necessitated by the exponential behavior of molecular state transitions under non-equilibrium conditions, including positive versus negative mass action kinetics and solute-induced perturbations to the hydrogen bonds of solvating water versus kT.

The process of drug discovery is very complex and requires an interdisciplinary effort to design effective and commercially feasible drugs. The objective of drug design is to find a chemical compound that can fit to a specific cavity on a protein target both geometrically and chemically. After passing the animal tests and human clinical trials, this compound becomes a drug available to patients. The conventional drug design methods include random screening of chemicals found in nature or synthesized in laboratories. The problems with this method are long design cycle and high cost. Modern approach including structure-based drug design with the help of informatic technologies and computational methods has speeded up the drug discovery process in an efficient manner. Remarkable progress has been made during the past five years in almost all the areas concerned with drug design and discovery. An improved generation of softwares with easy operation and superior computational tools to generate chemically stable and worthy compounds with refinement capability has been developed. These tools can tap into cheminformation to shorten the cycle of drug discovery, and thus make drug discovery more cost-effective. A complete overview of drug discovery process with comparison of conventional approaches of drug discovery is discussed here. Special emphasis is given on computational approaches for drug discovery along with salient features and applications of the softwares used in de novo drug designing. How to Cite this Article Pubmed Style Baldi A. Approaches for Drug Design and Discovery: An SRP. 2010; 1(1): 99-105. doi:10.4103/0975-8453.59519 Web Style Baldi A. Approaches for Drug Design and Discovery: An http://www.sysrevpharm.org/?mno=302644637 [Access: March 28, 2021]. doi:10.4103/0975-8453.59519 AMA (American Medical Association) Style Baldi A. Approaches for Drug Design and Discovery: An SRP. 2010; 1(1): 99-105. doi:10.4103/0975-8453.59519 Vancouver/ICMJE Style Baldi A. Approaches for Drug Design and Discovery: An SRP. (2010), [cited March 28, 2021]; 1(1): 99-105. doi:10.4103/0975-8453.59519 Harvard Style Baldi A (2010) Approaches for Drug Design and Discovery: An SRP, 1 (1), 99-105. doi:10.4103/0975-8453.59519 Turabian Style Baldi A. 2010. Approaches for Drug Design and Discovery: An Systematic Reviews in Pharmacy, 1 (1), 99-105. doi:10.4103/0975-8453.59519 Chicago Style Baldi A. Computational Approaches for Drug Design and Discovery: An Overview. Systematic Reviews in Pharmacy 1 (2010), 99-105. doi:10.4103/0975-8453.59519 MLA (The Modern Language Association) Style Baldi A. Computational Approaches for Drug Design and Discovery: An Overview. Systematic Reviews in Pharmacy 1.1 (2010), 99-105. Print. doi:10.4103/0975-8453.59519 APA (American Psychological Association) Style Baldi A (2010) Approaches for Drug Design and Discovery: An Systematic Reviews in Pharmacy, 1 (1), 99-105. doi:10.4103/0975-8453.59519

Discovering a new drug is a complex but sequential process from discovery to preclinical development, followed with clinical drug development. It has been estimated that ∼ 87% of the phase III failures are accounted for either due to lack of efficacy (66%) or due to safety issues (21%). Majority of these failures are for compounds targeted for novel mechanisms of actions with unmet medical need, in particular, oncology and neurodegenerative disorders. Some of the reasons for these failures can be attributed to lack of appropriate preclinical animal models, biomarkers/surrogate markers, and effective pharmacokinetic (PK)–pharmacodynamic (PD) evaluation during early drug discovery. Translational research that integrates computer‐aided drug design (CADD), PK, PD, drug metabolism (DM), and drug transport along with biomarkers and humanized animal models are instrumental in making informed decisions from early drug discovery through clinical development. The ability to correlate drug effect through modeling and simulations starts from early drug discovery and preclinical evaluation, including use of novel biomarkers. Such models validate the PK and PD relationships and provide a basis for their applications and guide the Phase I through Phase III clinical trials more effectively, minimizing the late stage failures. Thus, PK–PD evaluation has become an integral part of drug discovery and provides valuable insights to aid in optimizing the next steps for drug development. This chapter is focused on translational drug discovery research with particular emphasis on selective utilization of CADD; absorption, distribution, metabolism, and excretion; toxicology; PK; and PD evaluations, which identify potential liabilities early so as to minimize late‐stage failures during drug development. This chapter also provides a brief overview on means and measures that can be adopted to integrate early drug discovery research along with efficacy and safety biomarkers for meaningful transition to drug development.

Proteomic Approaches in Drug Discovery and Development

KRAS and BRAF targets are involved in the epidermal growth factor receptor pathway. KRAS and BRAF targets are the most frequent driver mutations in cancer. The objective of the study was to present the recent developments in the KRAS target and the BRAF target. KRAS target and BRAF target were analyzed by US patent analysis. All US granted patent documents from January 2002 to November 2021 were retrieved. The results showed both KRAS and BRAF targets to be attractive targets for developing anticancer drugs. The technology of RNA interference has been developed for drug discovery related to the KRAS target. Our study indicates that the structural screening of inhibitors between the KRAS target and the BRAF target should be an inverse option. The chemical structures of inhibitors of BRAF target exhibited a unique classification of C07D405. The inhibitors of BRAF target could be used for the treatment of various cancers. However, the inhibitors of KRAS target did not show this feature. The present study provides new insight into drug discovery involving KRAS and BRAF targets.

Drug discovery for challenging drug targets necessitates the proteomic complexities of the cellular milieu for proper folding and function. Therefore, the conventional biophysical methods are not sufficient due to the artificial acellular environment they impose on the target. Cell target engagement is a powerful paradigm in drug discovery, as measures transitions in the thermodynamic state of target protein during engagement with drug molecules. The currently available luminescence-based cell target engagement methods are limited to measurement of end-point signal, poor utility across multiple challenging target families and insufficient resolution and integration for kinetic and mechanistic readouts. We have developed a highly sensitive and versatile fluorescence-based cell target engagement strategy designed for challenging drug targets. The technology enables interrogation of drug targets without interfering with folding, localization and function - all within the physiological milieu of the cell. Importantly, this new method allows for quantitation and monitoring of cell target engagement in real time using live cells. The system seamlessly integrates with readily available real-time systems such as QuantStudio, thus making it highly sensitive and scalable. We share data on challenging drug targets such as Myc, MAPK1, UBE2N and KRAS, and we demonstrate utility of this new methodology for future drug discovery. Citation Format: Ivan Babic, Nikolas Bryan, Claire Cunningham, Avery Sampson, Daniel Starczynowski, Elmar Nurmemmedov. Live cell real-time quantification of drug-target engagement for rapid drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5480.

The attrition rate in drug development is being reduced by continuous advances in science and technology introduced by various academic institutions and pharmaceutical companies. This has been certainly noticeable in reducing the frequency with which unfavorable absorption, distribution, metabolism, and elimination (ADME) characteristics of any candidate drug causes failure in clinical development. Nonetheless, it is important that the objectives in reducing attrition during later stages of development are matched by information generated in the earliest stage of discovery. In this review, we summarize the methodologies employed during the early stages of drug discovery and discuss new findings in the areas of (1) drug metabolism enzymes, (2) the contribution of cytochrome P450 enzymes (P450, CYP) to hepatic metabolism, (3) prediction of hepatic intrinsic clearance, (4) reaction phenotyping, and (5) the metabolic differences between highly homologous enzymes such as CYP3A4 and CYP3A5. The total contribution of P450 and UDP-glucuronosyltransferases to drug metabolism is reported to be more than 80%; therefore, glucuronidation is increasingly recognized as an important clearance pathway in addition to that of P450 enzymes. When estimating the contribution of P450, interpreting the results of inhibition studies using a single P450 inhibitor can lead to false conclusions. For instance, 1-aminobenzotriazole and SKF-525A have a varying range of IC(50) values for inhibition of drug exidation-reaction by different CYP450 enzymes. There are disparities between methodologies at early stage drug discovery and late stage development. For example, although the drug depletion approach for the prediction of hepatic intrinsic clearance may not be desirable at late stages of development, it is suitable at the early drug discovery stage since kinetic characterization and measurement of specific drug metabolites are not required. Data from protein binding assays in plasma and/or liver microsomes is an integral part to predicting hepatic clearance; therefore, the prediction methods for protein binding have been addressed in terms of automation and in silico prediction. The approach to reaction phenotyping using recombinant P450 microsome data are reviewed as this approach enables combining the drug depletion method with appropriate scaling factors to predict clearance values. CYP3A enzymes have broad substrate specificities and are responsible for the oxidative metabolism of more than 50% of clinically used drugs. Although CYP3A4 is the most abundant CYP3A isoform in adult human liver, CYP3A5 may contribute more to CYP3A-mediated drug oxidation by human liver microsomes than CYP3A4 does, especially in Japanese subjects, who typically have a relatively high frequency of genetic CYP3A5 expression. Lack of efficacy and presence of serious side effects in some sub-group of patients remain the biggest sources of drug failure at late stage of drug development. Advances in appreciation of inter-individual variabilities in ADME, by creation of virtual individuals and use of appropriate information from early discovery may lead to a better anticipation of variable clinical and toxicological outcome following administration of any new drug candidate. Thus may also help with dosing strategies which minimize the potential side effects and maximize the clinical benefits. Accordingly, front-loading of efforts for characterizing the candidate drugs at early stages of discovery is recommended.

How can We Discover Safer Drugs?

Introduction The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery faces growing challenges in terms of time, cost, and efficacy, there is a pressing need to integrate these emerging technologies to enhance the discovery process. Areas covered In this perspective, the authors explore the role of AI and ML in modern early drug discovery and discuss their application in drug target identification, compound screening, and biomarker discovery. This article is based on a thorough literature search using the PubMed database to identify relevant studies that highlight the use of AI/ML models in computational chemistry, systems biology, and data-driven approaches to drug development. Emphasis is placed on how these technologies address key challenges such as data integration, predictive performance, and cost-efficiency in the drug discovery pipeline. Expert opinion AI and ML have the potential to revolutionize early drug discovery by improving the accuracy and speed of identifying viable drug candidates. However, successful integration of these technologies requires overcoming challenges related to data quality, model interpretability, and the need for interdisciplinary collaboration.

Preclinical assessment of direct drug-target engagement and analysis of binding kinetics within the cellular environment is essential for development of safer and more effective therapeutics. Infusion of such critical data into early drug discovery would significantly reduce the failure rate of new drug candidates, accelerate drug discovery, and validate repurposing of existing drugs. Described here is a novel drug-target engagement technology that can sensitively interrogate direct binding of drugs to cellular, bacterial, or viral protein targets within the physiological environment. Micro-Tag cell target engagement technology is based on complementation of a small 15-amino acid subunit with a large subunit into an active RNA-processing enzyme. The small subunit can be cloned to any drug target for transient or stable expression using existing tools such as CRISPR. Upon complementation, the active enzyme cleaves a FRET-based oligonucleotide substrate resulting in rapid generation of fluorescent signal that can be quantified in real time. The Micro-Tag technology enables in-cell quantitation of drug target levels using qPCR systems. This is the next-generation of target engagement technology that allows for real-time monitoring of drug-target interaction in the cell. We show here data demonstrating direct engagement of several reference compounds and novel small molecules with high-profile cancer targets such as K-RAS, MTH1, EGFR, and UBE2N. Selectivity of these drugs to the targets in the cell is further delineated using their mutant counterparts as well as inactive stereoisomer compounds. The Micro-Tag cell target engagement technology provides the power of cell target engagement to a large family of target proteins. It can be employed for high-throughput screens directed at initial on-target ranking or med-chem optimization of drug candidates. It is amenable for interrogation of drug candidates across various modalities: small molecules, peptides, antibodies and PROTACs. Importantly, this next-generation target engagement technology seamlessly integrates with qPCR systems, fluorescence microscopy, live-cell microscopy, FACS analysis, and offers multiplexing capability, thus allowing for further mechanistic insight. Citation Format: Ivan Babic, Nikolas Bryan, Claire Cunningham, Avery Sampson, Daniel Starczynowski, Elmar Nurmemmedov. Real-time cellular target engagement and protein quantification for drug discovery [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 2759.

Brain and plasma exposure profiling in early drug discovery using cassette administration and fast liquid chromatography-tandem mass spectrometry

This study aims to systematically review and analyze in vitro approaches for evaluating bioactive natural compound–target interactions in early drug discovery, with myristicin highlighted as a representative example. Employing a qualitative descriptive research design, this study utilized a library-based method through the comprehensive analysis of scientific articles, books, and reports published between 2015 and 2025. Data collection involved literature screening and document analysis from peer-reviewed journals, while data analysis followed an inductive thematic approach encompassing identification, reduction, categorization, and synthesis of findings. The results indicate that integrating in vitro assays with computational (in silico) modeling significantly enhances predictive accuracy, efficiency, and mechanistic understanding in early drug discovery. Myristicin demonstrated multitarget bioactivity, including anti-inflammatory, antimicrobial, antioxidant, and anticancer effects, primarily through modulation of COX-2, PI3K/Akt/mTOR, and P-glycoprotein pathways. These findings reinforce the theoretical framework of polypharmacology, supporting the concept that natural compounds often act through multi-target interactions rather than single-receptor specificity. The study contributes to pharmacognosy and molecular pharmacology by providing a conceptual and methodological model for integrating experimental and computational drug discovery approaches. The implications extend to both academic and industrial domains, promoting standardized in vitro validation and translational research for natural product–based drug development.