Early Stages Of Drug Development Research Articles

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.

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

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.

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.

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.

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.

In silico off-target profiling for enhanced drug safety assessment

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 Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical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

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

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

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

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

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

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

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

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

An Evolved Transformer Model for ADME/Tox Prediction

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

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

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

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

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

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

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