Leveraging Hypoxia-Activated Prodrugs to Prevent Drug Resistance in Solid Tumors

Exploring chronic and transient tumor hypoxia for predicting the efficacy of hypoxia-activated pro-drugs

Hypoxia, a low level of oxygen in the tissue, arises due to an imbalance between the vascular oxygen supply and oxygen demand by the surrounding cells. Typically, hypoxia is viewed as a negative marker of patients' survival, because of its implication in the development of aggressive tumors and tumor resistance. Several drugs that specifically target the hypoxic cells have been developed, providing an opportunity for exploiting hypoxia to improve cancer treatment. Here, we consider combinations of hypoxia-activated pro-drugs (HAPs) and two compounds that transiently increase intratumoral hypoxia: a vasodilator and a metabolic sensitizer. To effectively design treatment protocols with multiple compounds we used mathematical micro-pharmacology modeling and determined treatment schedules that take advantage of heterogeneous and dynamically changing oxygenation in tumor tissue. Our model was based on data from murine pancreatic cancers treated with evofosfamide (as a HAP) and either hydralazine (as a vasodilator), or pyruvate (as a metabolic sensitizer). Subsequently, this model was used to identify optimal schedules for different treatment combinations. Our simulations showed that schedules of HAPs with the vasodilator had a bimodal distribution, while HAPs with the sensitizer showed an elongated plateau. All schedules were more successful than HAP monotherapy. The three-compound combination had three local optima, depending on the HAPs clearance from the tissue interstitium, each two-fold more effective than baseline HAP treatment. Our study indicates that the three-compound therapy administered in the defined order will improve cancer response and that designing complex schedules could benefit from the use of mathematical modeling.

Cascade strategy for glucose oxidase-based synergistic cancer therapy using nanomaterials

Nanomaterial-based cancer therapy faces significant limitations due to the complex nature of the tumor microenvironment (TME). Starvation therapy is an emerging therapeutic approach that targets tumor cell metabolism using glucose oxidase (GOx). Importantly, it can provide a material or environmental foundation for other diverse therapeutic methods by manipulating the properties of the TME, such as acidity, hydrogen peroxide (H2O2) levels, and hypoxia degree. In recent years, this cascade strategy has been extensively applied in nanoplatforms for ongoing synergetic therapy and still holds undeniable potential. However, only a few review articles comprehensively elucidate the rational designs of nanoplatforms for synergetic therapeutic regimens revolving around the conception of the cascade strategy. Therefore, this review focuses on innovative cascade strategies for GOx-based synergetic therapy from representative paradigms to state-of-the-art reports to provide an instructive, comprehensive, and insightful reference for readers. Thereafter, we discuss the remaining challenges and offer a critical perspective on the further advancement of GOx-facilitated cancer treatment toward clinical translation.

Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis

Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.

Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer

The goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment outcomes when combined with immunotherapy, while evofosfamide alone showed no effects in an in vivo syngeneic model of colorectal cancer. However, the mechanisms behind the interaction between the tumor microenvironment in the context of oxygenation (hypoxic, normoxic), immunotherapy, and tumor cells are not fully understood. To begin to understand this issue, we develop a system of ordinary differential equations to simulate the growth and decline of tumors and their vascularization (oxygenation) in response to treatment with evofosfamide and immunotherapy (6 combinations of scenarios). The model is calibrated to data from in vivo experiments on mice implanted with colon adenocarcinoma cells and longitudinally imaged with [18F]-fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) to quantify hypoxia. The results show that evofosfamide is able to rescue the immune response and sensitize hypoxic tumors to immunotherapy. In the hypoxic scenario, evofosfamide reduces tumor burden by $ 45.07 \pm 2.55 $%, compared to immunotherapy alone, as measured by tumor volume. The model accurately predicts the temporal evolution of five different treatment scenarios, including control, hypoxic tumors that received immunotherapy, normoxic tumors that received immunotherapy, evofosfamide alone, and hypoxic tumors that received combination immunotherapy and evofosfamide. The average concordance correlation coefficient (CCC) between predicted and observed tumor volume is $ 0.86 \pm 0.05 $. Interestingly, the model values to fit those five treatment arms was unable to accurately predict the response of normoxic tumors to combination evofosfamide and immunotherapy (CCC = $ -0.064 \pm 0.003 $). However, guided by the sensitivity analysis to rank the most influential parameters on the tumor volume, we found that increasing the tumor death rate due to immunotherapy by a factor of $ 18.6 \pm 9.3 $ increases CCC of $ 0.981 \pm 0.001 $. To the best of our knowledge, this is the first study to mathematically predict and describe the increased efficacy of immunotherapy following evofosfamide.

Application of three-dimensional cell culture technology in screening anticancer drugs

The drug development process involves a variety of drug activity evaluations, which can determine drug efficacy, strictly analyze the biological indicators after the drug action, and use these indicators as the preclinical drug evaluation criteria. At present, most of the screening of preclinical anticancer drugs mainly relies on traditional 2D cell culture. However, this traditional technology cannot simulate the tumor microenvironment in vivo, let alone reflect the characteristics of solid tumors in vivo, and has a relatively poor ability to predict drug activity. 3D cell culture is a technology between 2D cell culture and animal experiments, which can better reflect the biological state in vivo and reduce the consumption of animal experiments. 3D cell culture can link the individual study of cells with the study of the whole organism, reproduce in vitro the biological phenotype of cells in vivo more greatly, and thus predict the activity and resistance of anti-tumor drugs more accurately. In this paper, the common techniques of 3D cell culture are discussed, with emphasis on its main advantages and application in the evaluation of anti-tumor resistance, which can provide strategies for the screening of anti-tumor drugs.

Assessing the therapeutic response of tumors to hypoxia-targeted prodrugs with an in silico approach

Tumor hypoxia is commonly recognized as a condition stimulating the progress of the aggressive phenotype of tumor cells. Hypoxic tumor cells inhibit the delivery of cytotoxic drugs, causing hypoxic areas to receive insufficient amounts of anticancer agents, which results in adverse treatment responses. Being such an obstruction to conventional therapies for cancer, hypoxia might be considered a target to facilitate the efficacy of treatments in the resistive environment of tumor sites. In this regard, benefiting from prodrugs that selectively target hypoxic regions remains an effective approach. Additionally, combining hypoxia-activated prodrugs (HAPs) with conventional chemotherapeutic drugs has been used as a promising strategy to eradicate hypoxic cells. However, determining the appropriate sequencing and scheduling of the combination therapy is also of great importance in obtaining favorable results in anticancer therapy. Here, benefiting from a modeling approach, we study the efficacy of HAPs in combination with chemotherapeutic drugs on tumor growth and the treatment response. Different treatment schedules have been investigated to see the importance of determining the optimal schedule in combination therapy. The effectiveness of HAPs in varying hypoxic conditions has also been explored in the study. The model provides qualitative conclusions about the treatment response, as the maximal benefit is obtained from combination therapy with greater cell death for highly hypoxic tumors. It has also been observed that the antitumor effects of HAPs show a hypoxia-dependent profile.

Fine-Feature Modifications to Strained Ruthenium Complexes Radically Alter Their Hypoxic Anticancer Activity†

In an earlier study of π-expansive ruthenium complexes for photodynamic and photochemo-therapies, it was shown that a pair of structural isomers differing only in the connection point of a naphthalene residue exhibited vastly different biological activity. These isomers are further explored in this paper through the activity of their functionalized derivatives. In normoxia, the inactive 2-NIP isomer (5) can be made as photocytotoxic as the active 1-NIP isomer (1) by functionalizing with methyl or methoxy groups, while methoxy variants of the 1-NIP isomer became inactive. In all cases, the singlet oxygen sensitization quantum yield was below 1%. Hypoxic photocytotoxicity was attenuated, with only three of the series showing any activity, notwithstanding the photodissociative ligands. The results here are consistent with the earlier findings in that seemingly minor structural modifications on the non-strained ligand can dramatically modulate the normoxic and hypoxic activity of these strained compounds and that these changes appear to exert a greater influence on photocytotoxicity than singlet oxygen sensitization or rates of photosubstitution in cell-free conditions would suggest.

Shuyu pills inhibit immune escape and enhance chemosensitization in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is characterized by dysregulation of the immune microenvironment and the development of chemoresistance. Specifically, expression of the programmed cell death protein 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) axis, an immune checkpoint, may lead to tumour immune escape, resulting in disease progression. The latest research shows that tumour immune escape may be caused by the upregulation of PD-L1 mediated by hypoxia-inducible factor-1 alpha (HIF-1α), and simultaneous inhibition of HIF-1α and PD-L1 has the potential to enhance the host’s antitumour immunity. Moreover, inhibition of the PD-1/PD-L1 axis may mitigate tumour chemoresistance. Shuyu pills (SYPs) contain immunity-enhancing and antitumour components, making them a potential HCC treatment.

AIM

To investigate the efficacy of SYPs for HCC treatment via simultaneous HIF-1α and PD-L1 inhibition and the mechanism involved.

METHODS

A subcutaneous xenograft tumour model was first established in BALB/c nude mice by the subcutaneous injection of 1 × 10⁷ SMMC-7721 cells. Male mice (male, 5 weeks old; n = 24) were then randomly divided into the following four groups (n = 6): Control (0.9% normal saline), SYP (200 mg/kg), SYP + cisplatin (DDP) (200 mg/kg + 5 mg/kg DDP weekly via intraperitoneal injection), and DDP (5 mg/kg cisplatin weekly via intraperitoneal injection). The dose of saline or SYPs for the indicated mouse groups was 0.2 mL/d via intragastric administration. The tumour volumes and body weights of the mice were measured every 2 d. The mice were euthanized by cervical dislocation after 14 d of continuous treatment, and the xenograft tissues were excised and weighed. Western blot assays were used to measure the protein expression of HIF-1α, PD­1, PD-L1, CD4+ T cells, and CD8+ T cells in HCC tumours from mice. Quantitative reverse transcription polymerase chain reaction was used for real-time quantitative detection of PD-1, PD-L1, and HIF-1α mRNA expression. An immunofluorescence assay was conducted to examine the expression of CD4+ T cells and CD8+ T cells.

RESULTS

Compared to mice in the control group, those in the SYP and SYP + DDP groups exhibited reduced tumour volumes and tumour weights. Moreover, the protein and mRNA expression levels of the oncogene HIF­1α and that of the negative immunomodulatory factors PD-1 and PD-L1 were decreased in both the SYP and SYP + DDP groups, with the decrease effects being more prominent in the SYP + DDP group than in the SYP group (HIF-1α protein: Control vs SYP, P = 0.0129; control vs SYP + DDP, P = 0.0004; control vs DDP, P = 0.0152, SYP + DDP vs DDP, P = 0.0448; HIF-1α mRNA: control vs SYP, P = 0.0009; control vs SYP + DDP, P < 0.0001; control vs DDP, P = 0.0003, SYP vs SYP + DDP, P = 0.0192. PD-1 protein: Control vs SYP, P = 0.0099; control vs SYP + DDP, P < 0.0001, SPY vs SYP + DDP, P = 0.0009; SYP + DDP vs DDP, P < 0.0001; PD-1 mRNA: control vs SYP, P = 0.0002; control vs SYP + DDP, P < 0.0001; control vs DDP, P = 0.0003, SPY vs SYP + DDP, P = 0.0003; SYP + DDP vs DDP, P = 0.0002. PD-L1 protein: control vs SYP, P < 0.0001; control vs SYP + DDP, P < 0.0001; control vs DDP, P < 0.0001, SPY vs SYP + DDP, P = 0.0040; SYP + DDP vs DDP, P = 0.0010; PD-L1 mRNA: Control vs SYP, P < 0.0001; control vs SYP + DDP, P < 0.0001; control vs DDP, P < 0.0001, SPY vs SYP + DDP, P < 0.0001; SYP + DDP vs DDP, P = 0.0014). Additionally, the quantitative and protein expression levels of CD4+ T cells and CD8+ T cells were simultaneously upregulated in the SYP + DDP group, whereas only the expression of CD4+ T cells was upregulated in the SYP group. (CD4+ T cell quantitative: Control vs SYP + DDP, P < 0.0001, SYP vs SYP + DDP, P = 0.0005; SYP + DDP vs DDP, P = 0.0002. CD4+ T cell protein: Control vs SYP, P = 0.0033; Control vs SYP + DDP, P < 0.0001; Control vs DDP, P = 0.0021, SYP vs SYP + DDP, P = 0.0004; SYP + DDP vs DDP, P = 0.0006. Quantitative CD8+ T cells: Control vs SYP + DDP, P = 0.0013; SYP vs SYP + DDP, P = 0.0347; SYP + DDP vs DDP, P = 0.0043. CD8+ T cell protein: Control vs SYP + DDP, P < 0.0001; SYP vs SYP + DDP, P < 0.0001; SYP + DDP vs DDP, P < 0.0001). Finally, expression of HIF-1α was positively correlated with that of PD-1/PD-L1 and negatively correlated with the expression of CD4+ T cells and CD8+ T cells.

CONCLUSION

SYPs inhibit immune escape and enhance chemosensitization in HCC via simultaneous inhibition of HIF-1α and PD-L1, thus inhibiting the growth of subcutaneous xenograft HCC tumours.

Targeting Hypoxia: Hypoxia-Activated Prodrugs in Cancer Therapy

Hypoxia is an important characteristic of most solid malignancies, and is closely related to tumor prognosis and therapeutic resistance. Hypoxia is one of the most important factors associated with resistance to conventional radiotherapy and chemotherapy. Therapies targeting tumor hypoxia have attracted considerable attention. Hypoxia-activated prodrugs (HAPs) are bioreductive drugs that are selectively activated under hypoxic conditions and that can accurately target the hypoxic regions of solid tumors. Both single-agent and combined use with other drugs have shown promising antitumor effects. In this review, we discuss the mechanism of action and the current preclinical and clinical progress of several of the most widely used HAPs, summarize their existing problems and shortcomings, and discuss future research prospects.

The Hypoxia-Activated Prodrug TH-302: Exploiting Hypoxia in Cancer Therapy

Hypoxia is an important feature of most solid tumors, conferring resistance to radiation and many forms of chemotherapy. However, it is possible to exploit the presence of tumor hypoxia with hypoxia-activated prodrugs (HAPs), agents that in low oxygen conditions undergo bioreduction to yield cytotoxic metabolites. Although many such agents have been developed, we will focus here on TH-302. TH-302 has been extensively studied, and we discuss its mechanism of action, as well as its efficacy in preclinical and clinical studies, with the aim of identifying future research directions.

Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma

Rationale: Hypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial of non-resectable soft tissue sarcomas, TH-302 did not improve survival in combination with doxorubicin (Dox), possibly due to a lack of patient stratification based on hypoxic status. Therefore, we used magnetic resonance imaging (MRI) to identify hypoxic habitats and non-invasively follow therapies response in sarcoma mouse models. Methods: We developed deep-learning (DL) models to identify hypoxia, using multiparametric MRI and co-registered histology, and monitored response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (radiation-induced fibrosarcoma, RIF-1). Results: A DL convolutional neural network showed strong correlations (>0.76) between the true hypoxia fraction in histology and the predicted hypoxia fraction in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had a lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment that was not due to a reduction in hypoxic volumes. Conclusion: Artificial intelligence analysis of pre-therapy MR images can predict hypoxia and subsequent response to HAPs. This approach can be used to monitor therapy response and adapt schedules to forestall the emergence of resistance.

Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape

Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data.

Immune cells as tumor drug delivery vehicles

This review article describes the use of immune cells as potential candidates to deliver anti-cancer drugs deep within the tumor microenvironment. First, the rationale of using drug carriers to target tumors and potentially decrease drug-related side effects is discussed. We further explain some of the current limitations when using nanoparticles for this purpose. Next, a comprehensive step-by-step description of the migration cascade of immune cells is provided as well as arguments on why immune cells can be used to address some of the limitations associated with nanoparticle-mediated drug delivery. We then describe the benefits and drawbacks of using red blood cells, platelets, granulocytes, monocytes, macrophages, myeloid-derived suppressor cells, T cells and NK cells for tumor-targeted drug delivery. An additional section discusses the versatility of nanoparticles to load anti-cancer drugs into immune cells. Lastly, we propose increasing the circulatory half-life and development of conditional release strategies as the two main future pillars to improve the efficacy of immune cell-mediated drug delivery to tumors.

Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments

Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line-specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

The emergence of drug resistance is often an inevitable obstacle that limits the long-term effectiveness of clinical cancer chemotherapeutics. Although various forms of cancer cell-intrinsic mechanisms of drug resistance have been experimentally revealed, the role and the underlying mechanism of tumor microenvironment in driving the development of acquired drug resistance remain elusive, which significantly impedes effective clinical cancer treatment. Recent experimental studies have revealed a macrophage-mediated drug resistance mechanism in which the tumor microenvironment undergoes adaptation in response to macrophage-targeted colony-stimulating factor-1 receptor (CSF1R) inhibition therapy in gliomas. In this study, we developed a spatio-temporal model to quantitatively describe the interplay between glioma cells and CSF1R inhibitor-targeted macrophages through CSF1 and IGF1 pathways. Our model was used to investigate the evolutionary kinetics of the tumor regrowth and the associated dynamic adaptation of the tumor microenvironment in response to the CSF1R inhibitor treatment. The simulation result obtained using this model was in agreement with the experimental data. The sensitivity analysis revealed the key parameters involved in the model, and their potential impacts on the model behavior were examined. Moreover, we demonstrated that the drug resistance is dose-dependent. In addition, we quantitatively evaluated the effects of combined CSFR inhibition and IGF1 receptor (IGF1R) inhibition with the goal of designing more effective therapies for gliomas. Our study provides quantitative and mechanistic insights into the microenvironmental adaptation mechanisms that operate during macrophage-targeted immunotherapy and has implications for drug dose optimization and the design of more effective combination therapies. Mol Cancer Ther; 17(4); 814-24. ©2018 AACR.

Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.