Data-driven Drug Research Articles

Study on Drug Optimization for Patients with Comorbid Hypertension and Coronary Heart Disease

Comorbidity of hypertension and coronary heart disease (CHD) is a common and complex clinical condition, making drug treatment optimization crucial. Hypertension accelerates the development of CHD by promoting atherosclerosis and cardiac structural changes. Current pharmacological treatments include diuretics, ACE inhibitors, calcium channel blockers, and β-adrenergic antagonists for hypertension, as well as antiplatelet agents, statins, β-adrenergic antagonists, and ACE inhibitors for CHD. Drug optimization strategies involve individualized treatment, optimization of polypharmacy, and dynamic adjustment. Future directions focus on the development of innovative drugs and treatment methods, data-driven drug optimization techniques, and multidisciplinary collaboration models. These advancements will enhance the precision and effectiveness of drug therapy, thereby improving the overall health and quality of life of patients.

Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGE

Safe drug recommendation systems play a crucial role in minimizing adverse drug reactions and enhancing patient safety. In this research, we propose an innovative approach to develop a safety drug recommendation system by integrating the Salp Swarm Optimization-based Particle Swarm Optimization (SalpPSO) with the GraphSAGE algorithm. The goal is to optimize the hyper parameters of GraphSAGE, enabling more accurate drug-drug interaction prediction and personalized drug recommendations. The research begins with data collection from real-world datasets, including MIMIC-III, Drug Bank, and ICD-9 ontology. The databases provide comprehensive and diverse clinical data related to patients, diseases, and drugs, forming the foundation of a knowledge graph. It represents drug-related entities and their relationships, such as drugs, indications, adverse effects, and drug-drug interactions. The knowledge graph’s integration of patient data, disease ontology, and drug information enhances the system’s accuracy to predict drug-drug interactions as well as identifying potential detrimental drug reactions. The GraphSAGE algorithm is employed as the base model for learning node embeddings in the knowledge graph. To enhance its performance, we propose the SalpPSO algorithm for hyper parameter optimization. SalpPSO combines features from Salp Swarm Optimization and Particle Swarm Optimization, offering a robust and effective optimization process. The optimized hyper parameters lead to more reliable and accurate drug recommendation system. For evaluation, the dataset is split into training and validation sets and compared the performance of the modified GraphSAGE model with SalpPSO-optimized hyper parameters to the standard models. The experimental analysis conducted in terms of various measures proves the efficiency of the proposed safe recommendation system, offering valuable for healthcare experts in making more informed and personalized drug treatment decisions for patients.

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.

Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery.

The molecular basis of the anticancer effect of statins

Statins, widely used cardiovascular drugs that lower cholesterol by inhibiting HMG-CoA reductase, have been increasingly recognized for their potential anticancer properties. This study elucidates the underlying mechanism, revealing that statins exploit Synthetic Lethality, a principle where the co-occurrence of two non-lethal events leads to cell death. Our computational analysis of approximately 37,000 SL pairs identified statins as potential drugs targeting genes involved in SL pairs with metastatic genes. In vitro validation on various cancer cell lines confirmed the anticancer efficacy of statins. This data-driven drug repurposing strategy provides a molecular basis for the anticancer effects of statins, offering translational opportunities in oncology.

Data-driven drug repositioning using olfactory omics profiles: challenges and perspectives in neurodegeneration.

Data-driven drug repositioning using olfactory omics profiles: challenges and perspectives in neurodegeneration.

Phenotypic assessment of antiviral activity for spiro-annulated oxepanes and azepenes.

Evolutionary potential of viruses can result in outbreaks of well-known viruses and emergence of novel ones. Pharmacological methods of intervening the reproduction of various less popular, but not less important viruses are not available, as well as the spectrum of antiviral activity for most known compounds. In the framework of chemical biology paradigm, characterization of antiviral activity spectrum of new compounds allows to extend the antiviral chemical space and provides new important structure-activity relationships for data-driven drug discovery. Here we present a primary assessment of antiviral activity of spiro-annulated derivatives of seven-membered heterocycles, oxepane and azepane, in phenotypic assays against viruses with different genomes, virion structures, and genome realization schemes: orthoflavivirus (tick-borne encephalitis virus, TBEV), enteroviruses (poliovirus, enterovirus A71, echovirus 30), adenovirus (human adenovirus C5), hantavirus (Puumala virus). Hit compounds inhibited reproduction of adenovirus C5, the only DNA virus in the studied set, in the yield reduction assay, and did not inhibit reproduction of RNA viruses.

A novel combination treatment for fragile X syndrome predicted using computational methods.

Fragile X syndrome is a neurodevelopmental disorder caused by silencing of the fragile X messenger ribonucleotide gene. Patients display a wide spectrum of symptoms ranging from intellectual and learning disabilities to behavioural challenges including autism spectrum disorder. In addition to this, patients also display a diversity of symptoms due to mosaicism. These factors make fragile X syndrome a difficult syndrome to manage and suggest that a single targeted therapeutic approach cannot address all the symptoms. To this end, we utilized Healx's data-driven drug discovery platform to identify a treatment strategy to address the wide range of diverse symptoms among patients. Computational methods identified the combination of ibudilast and gaboxadol as a treatment for several pathophysiological targets that could potentially reverse multiple symptoms associated with fragile X syndrome. Ibudilast is an approved broad-spectrum phosphodiesterase inhibitor, selective against both phosphodiesterase 4 and phosphodiesterase 10, and has demonstrated to have several beneficial effects in the brain. Gaboxadol is a GABAA receptor agonist, selective against the delta subunit, which has previously displayed encouraging results in a fragile X syndrome clinical trial. Alterations in GABA and cyclic adenosine monophosphate metabolism have long since been associated with the pathophysiology of fragile X syndrome; however, targeting both pathways simultaneously has never been investigated. Both drugs have a good safety and tolerability profile in the clinic making them attractive candidates for repurposing. We set out to explore whether the combination of ibudilast and gaboxadol could demonstrate therapeutic efficacy in a fragile X syndrome mouse model. We found that daily treatment with ibudilast significantly enhanced the ability of fragile X syndrome mice to perform a number of different cognitive assays while gaboxadol treatment improved behaviours such as hyperactivity, aggression, stereotypy and anxiety. Importantly, when ibudilast and gaboxadol were co-administered, the cognitive deficits as well as the aforementioned behaviours were rescued. Moreover, this combination treatment showed no evidence of tolerance, and no adverse effects were reported following chronic dosing. This work demonstrates for the first time that by targeting multiple pathways, with a combination treatment, we were able to rescue more phenotypes in a fragile X syndrome mouse model than either ibudilast or gaboxadol could achieve as monotherapies. This combination treatment approach holds promise for addressing the wide spectrum of diverse symptoms in this heterogeneous patient population and may have therapeutic potential for idiopathic autism.

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting.

Abstract 5356: Collaborating to ensure data-driven drug discovery on the Cancer Genomics Cloud: Realizing the possibilities for MoDaC and ATOM

Abstract Introduction. The NCI-funded Cancer Genomics Cloud (CGC) and the NCI Predicative Oncology Model and Data Clearinghouse (MoDaC) advance NCI computing infrastructure and tools that aim to reduce the burden of cancer on patients. The CGC provides a collaborative cloud base computation infrastructure that collocates computation, bioinformatics workflows, and 3+ PB data to researchers. MoDaC provides a publicly available resource generated from the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) Program and the Accelerating Therapeutics for Opportunities in Medicine (ATOM Consortium). The NCI-sponsored MoDaC program aims to add machine learning toolsets to identifying novel treatments for Cancer Patients. We present our progress collaborating with MoDaC to make machine learning models available on the Cancer Genomics Cloud. Methods. Our Bioinformatics teams migrated MoDaC tools on the CGC, defined standards for releasing models, and collected recommendations for decreasing the time required to make MoDaC tools available on the Cancer Genomics Cloud. We mirrored the ATOM Modeling Pipeline (AMPL), a drug discovery platform, on the CGC by making the GIT repository cloud accessible through Jupyter Notebook access, supporting interactive analysis. We translated AMPL and JDACS4C models into CWL. Converting these ML models into CWL supports reproducible execution, scalable deployment, and computational portability. Results. The AMPL drug discovery platform on the CGC includes data ingestion & curation, featurization, model training & tuning, prediction generation, visualization & analysis functionality as Jupyter notebooks and CWL workflows. The release consists of chemo-informatics tools for integrating cancer treatment features in deep-learning graph models. JDACS4C ML Models ported to the CGC include classifiers (tumor and normal-tumor pairs), autoencoders (Gene Expression), drug response predictors (single and combination), and Multitask Convolutional Neural Networks (extract information from cancer pathology reports). Conclusion. We optimized the MoDaC Drug Discovery and Machine Learning tools into cloud-native resources on the CGC, supporting interactive and GUI-driven analysis. The release supports technical and newer users to machine learning, allowing access to a broader user base than those who traditionally have access to ML toolsets. These MoDaC toolsets will support pre-clinical study evaluation, treatment identification, and experimental design. Moreover, existing MoDaC-AMPL tutorials on the CGC support distributed ML-Drug Discovery training. Lastly, collaborating with MoDaC teams identified standardization approaches that can reduce the time and effort to make these tools widely available across the NIH-NCI computational infrastructure. Citation Format: Soner Koc, Vojislav Varjacic, Miona Rankovic, Marijeta Slavkovic-Ilic, Aleksandar Danicic, Sean Black, Naomi Ohashi, Titli Sarkar, Zelia Worman, Jack DiGiovanna, Brandi Davis-Dusenbery, Dennis A. Dean. Collaborating to ensure data-driven drug discovery on the Cancer Genomics Cloud: Realizing the possibilities for MoDaC and ATOM. [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 5356.

Electrical Remodeling in Right Ventricular Failure Due to Pulmonary Hypertension: Unraveling Novel Therapeutic Targets.

Arrhythmias in the setting of right-ventricular (RV) remodeling contribute to majority of deaths in patients with pulmonary hypertension. However, the underlying mechanism of electrical remodeling remains elusive, especially ventricular arrhythmias. Here, we analyzed the RV transcriptome of pulmonary arterial hypertension (PAH) patients with compensated RV or decompensated RV and identified 8 and 45 differentially expressed genes known to be involved in regulating the electrophysiological properties of excitation and contraction of cardiac myocytes, respectively. Transcripts encoding voltage-gated Ca2+ and Na+ channels were notably decreased in PAH patients with decompensated RV, along with significant dysregulation of KV and Kir channels. We further showed similarity of the RV channelome signature with two well-known animal models of PAH, monocrotaline (MCT)- and Sugen-hypoxia (SuHx)-treated rats. We identified 15 common transcripts among MCT, SuHx, and PAH patients with decompensated RV failure. In addition, data-driven drug repurposing using the channelome signature of PAH patients with decompensated RV failure predicted drug candidates that may reverse the altered gene expression. Comparative analysis provided further insight into clinical relevance and potential preclinical therapeutic studies targeting mechanisms involved in arrhythmogenesis.

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data.

Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Machine learning models to accelerate the design of polymeric long-acting injectables

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.

Data-driven drug discovery for drug repurposing

To improve the decreased efficiency of drug discovery and development, drug repurposing (also called drug repositioning) has been expected, that it is a strategy for identifying new medical indications for approved, investigational or suspended drugs. Particularly, according to the rapid expansion of medical and life science data and the remarkable technological progress of AI technology in recent years, the approach of computational drug repurposing has been attracted as one of the applications in data-driven drug discovery. Computational drug repurposing is a method of systematical and strategical research for identifying novel indication candidates and prioritizing the indication candidates based on the various profiles of drugs, genes, and diseases. In this review article, the typical data science techniques for data-driven drug repurposing, 1. drug-target interaction prediction, 2. transcriptomics-based approach by using differentially gene expression profiles, 3. natural language processing and word embedding, and their current status were summarized. We have also introduced a use case of data-driven drug repurposing for the PPARγ/α agonist Netoglitazone that we actually analyzed. In addition, as an excellent successful case of data-driven drug repurposing in recent years, we have also discussed a repurposing case reported by BenevolentAI in 2020, that Baricitinib has been identified as a potential intervention for COVID-19, based on immunomodulatory treatment by its mechanism of action as a JAK1 and JAK2 inhibition.

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

GRU-based de novo molecular generation and combinatorial virtual screening of OX1R antagonists

Orexin-1 receptor (OX1R) has been proved to play an important role in the regulation of emotions, addiction, panic, or anxiety, and thus been a promising drug target for the treatments of drug addiction, anxiety, and depression, pain, and obesity. In this work, GRU-based deep neural network combined with transfer learning was successfully used to build a molecular generation model of OX1R antagonists by using 2,066,376 drug-like molecules from ChEMBL database and 11525 known OX1R antagonists. The results showed that the GRU-based generation model can accurately grasp the SMILES grammar of the drug-like molecules and tend to generate potential OX1R antagonists after transfer learning. Then, graph convolutional network (GCN) with multi-head attention mechanism followed by a cascade of traditional ligand, receptor, and rule-based virtual screening was performed to screen potent OX1R antagonists from the generated molecules, which results in 23 de novo potential OX1R antagonists with good drug-like and druggability properties. Overall, this paper integrates the advantages of traditional and data-driven drug design methods and provides important references for the lead compound discovery of OX1R antagonists.

Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery.

The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data necessitate the use of cutting-edge methodologies. As a result, Artificial Intelligence-driven methods that have been shown to improve the efficiency and accuracy of drug discovery have grown in favor. The goal of this thorough analysis is to provide an overview of the drug discovery and development timeline, various approaches to drug design, and the use of Artificial Intelligence in many aspects of drug discovery. Traditional drug development approaches and their disadvantages have been explored in this paper, followed by an introduction to AI-based technology. Also, advanced methods used in Machine Learning and Deep Learning are examined in detail. A few examples of big data research that has transformed the field of medication discovery have also been presented. Also covered are the many databases, toolkits, and software available for constructing Artificial Intelligence/Machine Learning models, as well as some standard model evaluation parameters. Finally, recent advances and uses of Machine Learning and Deep Learning in drug discovery are thoroughly examined, along with their limitations and future potential. Artificial Intelligence-based technologies enhance decision-making by utilizing the abundantly available high-quality data, thereby reducing the time and cost involved in the process. We anticipate that this review would be useful to researchers interested in Artificial Intelligencebased drug development.

A Consensus Compound/Bioactivity Dataset for Data-Driven Drug Design and Chemogenomics.

Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.

Process Innovation on Drug Discovery and Development by Digital Transformation

For improving low R&D efficiency in drug discovery and development, digital transformation (DX) based on big-data and data science technologies, such as machine learning, artificial intelligence and modeling & simulation, has been implemented in pharmaceutical and biotech industries. The process innovation by DX, called as data-driven drug discovery and development (D5), is supporting the rational decision making from exploratory research to late-stage clinical development, and enables identification of novel target molecules, optimization of lead compounds, clarification of pharmacological or toxicological mechanism-of-action, prioritization of development indications, facilitating of translational research, optimization of clinical trials, and others. In this presentation, I will explain the effectiveness and the technical issues of D5 approach by showing some case-studies related to computational drug repurposing as a good practice in D5 approach. And also, I will show the current application of deep learning and transfer learning for automation and speed-up of image data analytics in non-clinical pharmacological studies and drug screening. The D5 approach is an innovative research framework for leading to successful drug discovery and development by improving the R&D efficiency and changing of research process.

Double Dare: Exscientia Expands AI Platform into Antibody Design

GEN EdgeVol. 4, No. 1 Artificial IntelligenceDouble Dare: Exscientia Expands AI Platform into Antibody DesignBritish pioneer of data-driven drug discovery eyes doubling the number of potential druggable sites, as well as a future move into more complex biologicsAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:29 Nov 2022https://doi.org/10.1089/genedge.4.1.151AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 4Issue 1Jan 2022 Information© 2022 by GEN PublishingTo cite this article:Alex Philippidis.Double Dare: Exscientia Expands AI Platform into Antibody Design.GEN Edge.Jan 2022.896-902.http://doi.org/10.1089/genedge.4.1.151Published in Volume: 4 Issue 1: November 29, 2022PDF download

Multi-modal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution.

Gaining insight into the heterogeneity of nanoparticle drug distribution within tumors would improve both design and clinical translation of nanomedicines. There is little data showing the spatio-temporal behavior of nanomedicines in tissues as current methods are not able to provide a comprehensive view of the nanomedicine distribution, released drug or its effects in the context of a complex tissue microenvironment.Methods: A new experimental approach which integrates the molecular imaging and bioanalytical technologies MSI and IMC was developed to determine the biodistribution of total drug and drug metabolite delivered via PLA-PEG nanoparticles and to overlay this with imaging of the nanomedicine in the context of detailed tumor microenvironment markers. This was used to assess the nanomedicine AZD2811 in animals bearing three different pre-clinical PDX tumors.Results: This new approach delivered new insights into the nanoparticle/drug biodistribution. Mass spectrometry imaging was able to differentiate the tumor distribution of co-dosed deuterated non-nanoparticle-formulated free drug alongside the nanoparticle-formulated drug by directly visualizing both delivery approaches within the same animal or tissue. While the IV delivered free drug was uniformly distributed, the nanomedicine delivered drug was heterogeneous. By staining for multiple biomarkers of the tumor microenvironment on the same tumor sections using imaging mass cytometry, co-registering and integrating data from both imaging modalities it was possible to determine the features in regions with highest nanomedicine distribution. Nanomedicine delivered drug was associated with regions higher in macrophages, as well as more stromal regions of the tumor. Such a comparison of complementary molecular data allows delineation of drug abundance in individual cell types and in stroma.Conclusions: This multi-modal imaging solution offers researchers a better understanding of drug and nanocarrier distribution in complex tissues and enables data-driven drug carrier design.