Efficient Drug Discovery Research Articles

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction.

Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches such as the conformal predictor framework equip conventional models with a more rigorous assessment of confidence for individual point predictions. In this paper, we extend the inductive conformal prediction (ICP) framework for interaction data, in particular the compound-target binding affinity prediction task. The new framework is based on dynamically defined calibration sets that are specific for each testing pair and provides prediction assessment in the context of calibration pairs from its compound-target neighbourhood, enabling improved estimates based on the local properties of the prediction model. The effectiveness of the approach is benchmarked on several publicly available datasets and tested in realistic use-case scenarios with increasing levels of difficulty on a complex compound-target binding affinity space. We demonstrate that in such scenarios, novel approach combining applicability domain paradigm with conformal prediction framework, produces superior confidence assessment with valid and more informative prediction regions compared to other state-of-the-art conformal prediction approaches. Dataset and the code are available on GitHub (https://github.com/mlkr-rbi/dAD). Supplementary data are available at Bioinformatics online.

Hecogenin a Plant Derived Small Molecule as an Antagonist to BACE-1: A Potential Target for Neurodegenerative Disorders.

The field of drug discovery has recognized the significance of computer-aided drug design. Recent advancements in structure identification and characterization, bio-computational science and molecular biology have significantly contributed to the development of novel treatments for various diseases. Alzheimer's disease is prevalent in over 50 million affected people, with the pathological condition of amyloidal plaque formation by the beta-amyloidal peptide that results in lesions of the patient's brain, thus making the target prediction and treatment a hurdle. In this study, we evaluated the potential of 54 bioactive compounds from Justicia adhatoda L. and Sida cordifolia L. identified through LC-MS/MS against the β-site amyloid precursor cleaving enzyme (beta-secretase) that results in the formation of amyloidal plaques. To study the drug-likeness of the phytocompounds, Lipinski's rule of five for ADME profiling and toxicity prediction was performed. Molecular docking was performed using auto-dock tool of PyRx software; molecular dynamic simulations were performed using the Schrodinger suite. Molecular docking against BACE-1 protein revealed that hecogenin, identified from S. cordifolia has a broad spectrum of pharmacological applications and a binding affinity score of -11.3 kcal/Mol. The Hecogenin-BACE-1 protein complex was found to be stable after 30 ns of MD simulation, resulting in its substantial stability. Further studies focusing on the in vivo neuroprotective activity of hecogenin against the disease will pave the way for efficient drug discovery from natural sources in a precise manner.

Multi-Drug Transfer Learning Models for Efficient Pre-clinical Drug Discovery

Multi-Drug Transfer Learning Models for Efficient Pre-clinical Drug Discovery

Contrastive learning in protein language space predicts interactions between drugs and protein targets

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

Abstract 4809: A novel microfluidic platform for PDAC organoid culture and drug screening

Abstract Pancreatic Ductal Adenocarcinoma (PDAC), the most common pancreatic cancer type, will become the second leading cause of cancer-related deaths by 2030 with mortality rates of up to 93%. Current standard-of-care for patients with PDAC includes chemotherapeutic regimens and pancreatic cancer surgery. However, only 20% of the patients are eligible for surgery due to late diagnosis. Although chemotherapeutic regimens are the leading treatment to PDAC patients, these are still very limited and the development of resistance to treatment is often observed. PDAC tumors are characterized by high-density stroma and hypovascularization, therefore these present high interstitial pressure and hypoxia. These features potentially interfere with the efficiency of chemotherapeutic drugs and highlight the urgent need for novel PDAC screening platforms. Here, we describe the establishment of PDAC organoid cultures in the MIMETAS OrganoPlate®. The aim of this project was to determine the treatment response of PDAC organoids in mono-, and co-culture with pancreatic stellate cells (PSCs) under hypoxic and normoxic conditions. To recapitulate in-vivo like conditions, the 2-lane OrganoPlate® from MIMETAS was used to study organoid growth and sensitivity to treatment. Several standard of care (chemo-)therapeutics were tested on PDAC organoids embedded within an extracellular matrix in the Organoplate® 2-lane. These were exposed to chemotherapeutic treatments for 72h. PDAC organoids showed an overall better survival when grown in co-culture with PSCs. Interestingly, organoids grown in co-culture showed a higher survival rate under hypoxic conditions. In contrast, when grown in monoculture cell viability was higher or similar under normoxic conditions than in hypoxia for the different chemotherapeutics. The OrganoPlate® 2-lane provides an excellent platform for (co-) cultivation and high-throughput phenotypic drug screening of PDAC organoids, thereby potentially enabling the development of novel in-vivo like model systems for efficient patient stratification and drug discovery. Citation Format: Marlene Geyer, Daniel Schreyer, Lisa-Marie Gaul, Désiréé Goubert, Susanne Pfeffer, Christian Pilarsky, Karla S. Queiroz. A novel microfluidic platform for PDAC organoid culture and drug screening. [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 4809.

Single-molecule fingerprinting of protein-drug interaction using a funneled biological nanopore

In drug discovery, efficient screening of protein-drug interactions (PDIs) is hampered by the limitations of current biophysical approaches. Here, we develop a biological nanopore sensor for single-molecule detection of proteins and PDIs using the pore-forming toxin YaxAB. Using this YaxAB nanopore, we demonstrate label-free, single-molecule detection of interactions between the anticancer Bcl-xL protein and small-molecule drugs as well as the Bak-BH3 peptide. The long funnel-shaped structure and nanofluidic characteristics of the YaxAB nanopore enable the electro-osmotic trapping of diverse folded proteins and high-resolution monitoring of PDIs. Distinctive nanopore event distributions observed in the two-dimensional (ΔI/Io-versus-IN) plot illustrate the ability of the YaxAB nanopore to discriminate individual small-molecule drugs bound to Bcl-xL from non-binders. Taken together, our results present the YaxAB nanopore as a robust platform for label-free, ultrasensitive, single-molecule detection of PDIs, opening up a possibility for low-cost, highly efficient drug discovery against diverse drug targets.

The combination of machine learning and transcriptomics reveals a novel megakaryopoiesis inducer, MO-A, that promotes thrombopoiesis by activating FGF1/FGFR1/PI3K/Akt/NF-κB signaling

Radiation-induced thrombocytopenia (RIT) occurs widely and causes high mortality and morbidity in cancer patients who receive radiotherapy. However, specific drugs for treating RIT remain woefully inadequate. Here, we first developed a drug screening model using naive Bayes, a machine learning (ML) algorithm, to virtually screen the active compounds promoting megakaryopoiesis and thrombopoiesis. A natural product library was screened by the model, and methylophiopogonanone A (MO-A) was identified as the most active compound. The activity of MO-A was then validated in vitro and showed that MO-A could markedly induce megakaryocyte (MK) differentiation of K562 and Meg-01 cells in a concentration-dependent manner. Furthermore, the therapeutic action of MO-A on RIT was evaluated, and MO-A significantly accelerated platelet level recovery, platelet activation, megakaryopoiesis, MK differentiation in RIT mice. Moreover, RNA-sequencing (RNA-seq) indicated that the PI3K cascade was closely related to MK differentiation induced by MO-A. Finally, experimental verification demonstrated that MO-A obviously induced the expression of FGF1 and FGFR1, and increased the phosphorylation of PI3K, Akt and NF-κB. Blocking FGFR1 with its inhibitor dovitinib suppressed MO-A-induced MK differentiation, and PI3K, Akt and NF-κB phosphorylation. Similarly, inhibition of PI3K-Akt signal pathway by its inhibitor LY294002 suppressed MK differentiation, and PI3K, Akt and NF-κB phosphorylation induced by MO-A. Taken together, our study provides an efficient drug discovery strategy for hematological diseases, and demonstrates that MO-A is a novel countermeasure for treating RIT through activation of the FGF1/FGFR1/PI3K/Akt/NF-κB signaling pathway.

Spatially selective cell treatment and collection for integrative drug testing using hydrodynamic flow focusing and shifting.

Hydrodynamic focusing capable of readily producing and controlling laminar flow facilitates drug treatment of cells in existing microfluidic culture devices. However, to expand applications of such devices to multiparameter drug testing, critical limitations in current hydrodynamic focusing microfluidics must be addressed. Here we describe hydrodynamic focusing and shifting as an advanced microfluidics tool for spatially selective drug delivery and integrative cell-based drug testing. We designed and fabricated a co-flow focusing, three-channel microfluidic device with a wide cell culture chamber. By controlling inlet flow rates of sample and two side solutions, we could generate hydrodynamic focusing and shifting that mediated precise regulation of the path and width of reagent and drug stream in the microfluidic device. We successfully validated a hydrodynamic focusing and shifting approach for spatially selective delivery of DiI, a lipophilic fluorophore, and doxorubicin, a chemotherapeutic agent, to tumor cells in our device. Moreover, subsequent flowing of a trypsin EDTA solution over the cells that were exposed to doxorubicin flow allowed us to selectively collect the treated cells. Our approach enabled downstream high-resolution microscopy of the cell suspension to confirm the nuclear delivery of doxorubicin into the tumor cells. In the device, we could also evaluate in situ the cytotoxic effect of doxorubicin to the tumor cells that were selectively treated by hydrodynamic flow focusing and shifting. These results show that hydrodynamic focusing and shifting enable a fast and robust approach to spatially treat and then collect cells in an optimized microfluidic device, offering an integrative assay tool for efficient drug screening and discovery.

Recent Advances in the Synthesis of Nitrogen Heterocycles Using β-Nitrostyrenes as Substrates

Abstract:: The exploration of synthetic methodologies that allow rapid access to a wide variety of N-heterocycles is of critical importance to the medicinal chemist as it provides the ability to expand the available drug-like chemical space and drives more efficient drug discovery programs. β-Nitrostyrenes, as unique active intermediates, have been widely applied in synthetic organic chemistry because of their versatile utility as pharmaceutical agents and agrochemicals. In this review, we summarize the recent development and application of the elegant and efficient methods that enable the concise synthesis of N-heterocycles from β-nitrostyrenes and various partners in a step- and atom-economic manner, including cascade reactions, C-H activation, regio- and stereoselective syntheses, as well as other novel syntheses, which will potentially provide useful insights for further exploring and designing novel reactions.

Molecular Dynamics Simulations of Self-Assembling Colloids in Fed-State Human Intestinal Fluids and Their Solubilization of Lipophilic Drugs.

Bioavailability of oral drugs often depends on how soluble the active pharmaceutical ingredient is in the fluid present in the small intestine. For efficient drug discovery and development, computational tools are needed for estimating this drug solubility. In this paper, we examined human intestinal fluids collected in the fed state, with coarse-grained molecular dynamics simulations. The experimentally obtained concentrations in aspirated duodenal fluids from five healthy individuals were used in three simulation sets to evaluate the importance of the initial distribution of molecules and the presence of glycerides in the simulation box when simulating the colloidal environment of the human intestinal fluid. We observed self-assembly of colloidal structures of different types: prolate, elongated, and oblate micelles, and vesicles. Glycerides were important for the formation of vesicles, and their absence was shown to induce elongated micelles. We then simulated the impact of digestion and absorption on the different colloidal types. Finally, we looked at the solubilization of three model compounds of increasing lipophilicity (prednisolone, fenofibrate, and probucol) by calculating contact ratios of drug-colloid to drug-water. Our simulation results of colloidal interactions with APIs were in line with experimental solubilization data but showed a dissimilarity to solubility values when comparing fasted-/fed-state ratios between two of the APIs. This work shows that coarse-grained molecular dynamics simulation is a promising tool for investigation of the intestinal fluids, in terms of colloidal attributes and drug solubility.

High-throughput assay for effect screening of amphotericin B and bioactive components on filamentous Candida albicans.

The aim of this study was to develop a high-throughput robotic microtiter plate-based screening assay for Candida albicans, optimizing growth conditions to replicate the filamentous biofilm growth found in vivo, and subsequently, to demonstrate the assay by evaluating the effect of nutritional drinks alone and in combination with the antifungal amphotericin B (AmB). Candida albicans cultured in a defined growth medium showed filamentous growth in microcolonies, mimicking the morphology of oral mucosal disease (oral candidiasis). Addition of nutrient drinks containing fruit juices, fish oil and whey protein to the medium resulted in changed morphology and promoted growth as free yeast cells and with weak biofilm structures. Minimum inhibitory concentration of AmB on the biofilms was 0.25 μg ml-1 , and this was eightfold reduced (0.0038 μg ml-1 ) in the presence of the nutritional drinks. The established assay demonstrated applicability for screening of antifungal and anti-biofilm effects of bioactive substances on C. albicans biofilm with clinically relevant morphology. Candida albicans is the causative agent of the majority of fungal infections globally. The filamentous morphology of C. albicans and the ability to form biofilm are traits known to increase virulence and resistance towards antifungals. This study describes the development of a plate-based in vitro screening method mimicking the filamentous morphology of C. albicans found in vivo. The assay established can thus facilitate efficient antifungal drug discovery and development.

4-Phthalimidobenzenesulfonamide Derivatives as Acetylcholinesterase and Butyrylcholinesterase Inhibitors: DFTs, 3D-QSAR, ADMET, and Molecular Dynamics Simulation

Introduction: Alzheimer’s disease is a form of dementia which affects majority of the people. It is characterized by memory loss and other cognitive function disabilities and is one of the most challenging neurodegenerative disorders to treat because of its progressive nature. The disease affects millions of people all around the world, and the number of those affected is expanding every day. In the previous study, the 4-phthalimidobenzenesulfonamide derivatives were synthesized as AChE and BChE inhibitors, and here, we were aiming to further reporting in silico studies of these compounds for efficient drug discovery process and to find out the potential lead compounds. Methods: In silico characterization included density functional theory (DFT) studies, 3D-QSAR, ADMET properties, molecular docking, and molecular dynamic simulations. The geometries of all derivatives were optimized using B3LYP method and 6-311G basis set. Results: The findings of the current study revealed that 4-phthalimidobenzenesulfonamide derivatives exhibited a reactive electronic property which is essential for anticholinesterase activity. Moreover, optimized structures were subjected to molecular docking studies with targeted protein. The compounds 2c and 2g showed excellent binding score of −37.44 and −33.67 kJ/mol with BChE and AChE, respectively, and exhibited strong binding affinity. The potent derivatives produced stable complex with amino acid residues of active pocket of both BChE and AChE. The stability of protein-ligand complexes was determined by molecular dynamic simulation studies, and results were found in correlation with molecular docking findings. Conclusion: Findings of the current study suggested that these derivatives are potent inhibitors of cholinesterase enzyme.

Chip-DSF: A rapid screening strategy for drug protein targets

Identification of the drug target of lead compounds is an important means for rapid and efficient drug discovery. Protein chips are a high-throughput protein function analysis technology that has been widely used in screening drug protein targets in recent years. However, the verification of the results after high-throughput protein chip screening is still cumbersome. Based on our mature protein chip preparation platform, we prepared a protein chip containing 150 important high-frequency protein targets and used antibodies to prove the availability of the protein chip. To improve the accuracy of target screening, we combined the label-free differential scanning fluorimetry (DSF) with the protein chip, proposing the Chip–DSF strategy. Subsequently, we tested the method with small molecular ginsenoside-Rg2 (Rg2). The Chip–DSF strategy was used to successfully screen the potential target protein KRAS(G12C) of Rg2. Consistently, we found that Rg2 could inhibit NCI-H23 cell proliferation by inducing cell cycle arrest. Also, we found that Rg2 could reduce the amount of KRAS protein and inhibit the phosphorylation of KRAS downstream key signaling protein ERK1, RPS6, and P70S6K in NCI-H23 cells. Collectively, our Chip-DSF strategy could achieve rapid target verification which improved the accuracy and efficiency of target screening of protein chips.

Atomic zinc sites with hierarchical porous carbon for high-throughput chemical screening with high loading capacity and stability

Alzheimer’s disease (AD) is a neurodegenerative disease associated with aging, and the number of people affected is rapidly increasing. Abnormally hyperphosphorylated tau filaments and extracellular deposits of amyloid β-peptides (Aβ) fibrils are two important pathological hallmarks of AD. Currently, stopping the production of Aβ and blocking its aggregation is the main strategy for the treatment of AD. Turmeric is effective in treating neurodegenerative diseases, but there is no effective way to identify active compounds from their complicated chemical compositions. Instead of using conventional extraction and separation methods with low efficiency and time-consuming, our group tried to use atomic materials in high-throughput chemical screening due to their structural characteristics and the unique advantages of surface atomic. Herein, a novel atomic zinc sites with hierarchical porous carbon (Zn-HPC) was synthesized to quickly screen potential inhibitors of Aβ aggregation in turmeric. As-combined Aβ@Zn-HPC demonstrates superior storage stability and high selectivity, outperforming the most reported supporters for ligand fishing. Five compounds with strong affinity on Aβ@Zn-HPC were selected by high-performance liquid chromatography-hybrid linear ion trap/orbitrap mass spectrometer after incubation with turmeric extract. Finally, it was shown that curcumin and bisdemethoxycurcumin can inhibit Aβ aggregation by using thioflavin-T fluorescence assay and biolayer interferometry. A new application for the accurate identification of Aβ aggregation inhibitors from turmeric were developed based on the active compounds possessing binding affinity to Aβ to inhibit its aggregation. The developed method could provide a promising tool for efficient drug discovery from natural product resources.

Molecular contrastive learning of representations via graph neural networks

Molecular machine learning bears promise for efficient molecular property prediction and drug discovery. However, labelled molecule data can be expensive and time consuming to acquire. Due to the limited labelled data, it is a great challenge for supervised-learning machine learning models to generalize to the giant chemical space. Here we present MolCLR (Molecular Contrastive Learning of Representations via Graph Neural Networks), a self-supervised learning framework that leverages large unlabelled data (~10 million unique molecules). In MolCLR pre-training, we build molecule graphs and develop graph-neural-network encoders to learn differentiable representations. Three molecule graph augmentations are proposed: atom masking, bond deletion and subgraph removal. A contrastive estimator maximizes the agreement of augmentations from the same molecule while minimizing the agreement of different molecules. Experiments show that our contrastive learning framework significantly improves the performance of graph-neural-network encoders on various molecular property benchmarks including both classification and regression tasks. Benefiting from pre-training on the large unlabelled database, MolCLR even achieves state of the art on several challenging benchmarks after fine-tuning. In addition, further investigations demonstrate that MolCLR learns to embed molecules into representations that can distinguish chemically reasonable molecular similarities.

Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation.

For the discovery of new candidate molecules in the pharmaceutical industry, library synthesis is a critical step, in which library size, diversity, and time to synthesise are fundamental. In this work we propose stopped-flow synthesis as an intermediate alternative to traditional batch and flow chemistry approaches, suited for small molecule pharmaceutical discovery. This method exploits the advantages of both techniques enabling automated experimentation with access to high pressures and temperatures; flexibility of reaction times, with minimal use of reagents (μmol scale per reaction). In this study, we integrate a stopped-flow reactor into a high-throughput continuous platform designed for the synthesis of combinatory libraries with at-line reaction analysis. This approach allowed ∼900 reactions to be conducted in an accelerated timeframe (192 hours). The stopped flow approach used ∼10% of the reactants and solvents compared to a fully continuous approach. This methodology demonstrates a significantly improved synthesis success rate of smaller libraries by simplifying the implementation of cross-reaction optimisation strategies. The experimental datasets were used to train a feed-forward neural network (FFNN) model providing a framework to guide further experiments, which showed good model predictability and success when tested against an external set with fewer experiments. As a result, this work demonstrates that combining experimental automation with machine learning strategies can deliver optimised analyses and enhanced predictions, enabling more efficient drug discovery investigations across the design, make, test and analysis (DMTA) cycle.

Progress in molecular biology and translational science addressing the needs of nano-rare patients.

The healthcare systems in the developed economies were established primarily to address the more prevalent diseases and have been customized to support the provision of therapeutics to rare patients. However, with the ever-broader implementation of genomic sequencing, it is clear that there are significantly more disease-causing mutations in the human genome than realized and that many mutations are much rarer than current definitions of rare disease populations. Given this, I propose parsing patient populations and defining patient populations more precisely. Nano-rare patients are defined as patients having disease-causing mutations that are unique to a single patient or having a known worldwide prevalence of less than 30. These patient populations present unique challenges to healthcare systems that demand the development of novel models for delivery of therapeutics and novel, more efficient drug discovery technologies, such as antisense technology. The challenges presented by nano-rare patients, a novel non-profit model as a means of providing experimental treatments rather than the traditional commercial model, and progress in establishing a non-profit solution are discussed.

Relating Conformational Equilibria to Conformer-Specific Lipophilicities: New Opportunities in Drug Discovery.

Efficient drug discovery is based on a concerted effort in optimizing bioactivity and compound properties such as lipophilicity, and is guided by efficiency metrics that reflect both aspects. While conformation–activity relationships and ligand conformational control are known strategies to improve bioactivity, the use of conformer‐specific lipophilicities (logp) is much less explored. Here we show how conformer‐specific logp values can be obtained from knowledge of the macroscopic logP value, and of the equilibrium constants between the individual species in water and in octanol. This is illustrated with fluorinated amide rotamers, with integration of rotamer 19F NMR signals as a facile, direct method to obtain logp values. The difference between logp and logP optimization is highlighted, giving rise to a novel avenue for lipophilicity control in drug discovery.

OP279 Data Protection In The European Union Post-General Data Protection Regulation (GDPR): A Barrier Or An Enabler Of Pharmaceutical Innovation?

IntroductionThe expansion of health data offers exciting opportunities to support better and more efficient drug discovery, development and implementation. Data protection and governance provide the legal framework to balance safeguarding patients’ privacy with the benefits to society of medical research. Our aim is to highlight current legal barriers to the better use of health data and propose ways to address them.MethodsAnalysis of the relevant legislative texts was supplemented by interviews with external experts in data protection, health research, informatics and cyber security and a workshop with pharmaceutical industry members. We investigated the legal issues arising for six key activities along the pharmaceutical lifecycle, from identifying unmet need through to health technology assessment and pharmacovigilance.ResultsThe General Data Protection Regulation (GDPR) was introduced in May 2018 to Harmonise data protection across Europe. However, considerable ambiguity remains, particularly around the appropriate legal bases for data processing in the absence of consent: scientific research, public interest, or provision of health or social care. Other key themes included data subject rights, anonymization, compatibility of primary and secondary (re-)use of data, heterogeneity arising from divergent interpretation, the need for guidance on digital health, and the importance of trust.ConclusionsWe speculate which legal bases are most appropriate for the six pharmaceutical activities studied, but clear guidance and consensus is required. The GDPR was not designed to hamper scientific research, and the issues identified arose from uncertainties rather than barriers per se. Industry and academic researchers should therefore deal proactively with the prevailing uncertainties, share good practice, and engender trust by co-creating a code of conduct and outlining principles of responsible use. Engagement with patients will be critical in encouraging a shared understanding of the value to society of health data for research.