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Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

PEGylation is routinely used to extend the systemic circulation of various protein therapeutics and nanomedicines. Nonetheless, mounting evidence is emerging that individuals exposed to select PEGylated therapeutics can develop antibodies specific to PEG, i.e., anti-PEG antibodies (APA). In turn, APA increase both the risk of hypersensitivity to the drug as well as potential loss of efficacy due to accelerated blood clearance of the drug. Despite the broad implications of APA, the timescales and systemic specificity by which APA can alter the pharmacokinetics and biodistribution of PEGylated drugs remain not well understood. Here, we developed a physiologically based pharmacokinetic (PBPK) model designed to resolve APA’s impact on both early- and late-phase pharmacokinetics and biodistribution of intravenously administered PEGylated drugs. Our model accurately recapitulates PK and biodistribution data obtained from PET/CT imaging of radiolabeled PEG-liposomes and PEG-uricase in mice with and without APA, as well as serum levels of PEG-uricase in humans. Our work provides another illustration of the power of high-resolution PBPK models for understanding the pharmacokinetic impacts of anti-drug antibodies and the dynamics with which antibodies can mediate clearance of foreign species.

Supplementary Data Online

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79–1.49) and 1.54 (1.01–2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients.Significance:A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

Supplementary Text S1. Mathematical model and parameter estimation; Supplementary Table 1. Cohen's d point estimate and 95% confidence interval in effect size tests among patients with PD (Progressive Disease), SD (Stable Disease) or PR (Partial Response) responses; Supplementary Figure 1. The individual tumor growth profiles for all 599 individuals; Supplementary Figure 2. Kaplan-Meier curves of the overall survival starting from last tumor observation for each individual patient. The curves were categorized by evolutionary parameters and the number of cell subclones at diagnosis derived from the evolutionary model.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it fails to address response heterogeneity across metastases. This study argues that spatiotemporal heterogeneity across metastases and organ-specific response is informative for drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients from five phase III clinical trials. Initially, a metric Gower distance was applied to quantify response heterogeneity across metastases. Next, the spatiotemporal response heterogeneity across anatomic sites, therapies, and KRAS mutation status was assessed and examined for its association with drug efficacy and long-term patient survival. The response of metastatic lesions broadly differed across anatomic sites and therapies. About 60% of patients had at least one lesion respond contrarily from total tumor size. High interlesion heterogeneity was associated with shorter progression-free survival and overall survival. Targeted therapies (bevacizumab or panitumumab) combined with standard chemotherapy reduced interlesion heterogeneity and elicited more favorable effects from liver lesions (P < 0.001) than chemotherapy alone. Moreover, the favorable responses in liver metastases (> 30% shrinkage) were associated with extended patient overall survival (P < 0.001), in contrast to lesions in the lungs and lymph nodes. Altogether, the spatiotemporal response heterogeneity across metastases informed drug efficacy and patient survival, which could improve the current methods for treatment evaluation and patient prognosis.Significance:These findings support the modification of RECIST criteria to include individual lesion response to improve assessments of drug efficacy.

Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and a programmed death ligand 1 (PD-L1) Tumor Proportion Score ≥50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest that a subset of patients may benefit from pembrolizumab beyond progression; these results have not been validated in a prospective study. We constructed digital twins of patients and simulated clinical trials to predict the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions continue shrinking. With25,000 radiographic lesion measurements from500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab beyond progression or salvage chemotherapy. Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials predicted progression-free survival (PFS) from pembrolizumab beyond progression to be comparable with salvage chemotherapy in patients whose PD was due to nontarget progression. A PFS-optimized regimen may improve disease control rates ≥15%. Pembrolizumab beyond progression may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted.

Factors such as antibody clearance and target affinity can influence antibodies’ effective doses for specific indications. However, these factors vary considerably across antibody classes, precluding direct and quantitative comparisons. Here, we apply a dimensionless metric, the therapeutic exposure affinity ratio (TEAR), which normalizes the therapeutic doses by antibody bioavailability, systemic clearance and target-binding property to enable direct and quantitative comparisons of therapeutic doses. Using TEAR, we revisited and dissected the doses of up to 60 approved antibodies. We failed to detect a significant influence of target baselines, turnovers or anatomical locations on antibody therapeutic doses, challenging the traditional perceptions. We highlight the importance of antibodies’ modes of action for therapeutic doses and dose selections; antibodies that work through neutralizing soluble targets show higher TEARs than those working through other mechanisms. Overall, our analysis provides insights into the factors that influence antibody doses, and the factors that are crucial for antibodies’ pharmacological effects.

Background and Purpose: Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity. Experimental Approach: We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contribution of these factors to response heterogeneity are compared. Key Results: The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Drug potency strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib. Conclusion and Implications: The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.

To the extent that pharmacokinetics influence the effectiveness of nonliving therapeutics, so too do cellular kinetics influence the efficacy of Chimeric Antigen Receptor (CAR) -T cell therapy. Like conventional therapeutics, CAR-T cell therapies undergo a distribution phase upon administration. Unlike other therapeutics, however, this distribution phase is followed by subsequent phases of expansion, contraction, and persistence. The magnitude and duration of these phases unequivocally influence clinical outcomes. Furthermore, the "pharmacodynamics" of CAR-T cells is truly dynamic, as cells can rapidly become exhausted and lose their therapeutic efficacy. Mathematical models are among the translational tools commonly applied to assess, characterize, and predict the complex cellular kinetics and dynamics of CAR-T cells. Here, we provide a focused review of the cellular kinetics of CAR-T cells, the mechanisms underpinning their complexity, and the mathematical modeling approaches used to interrogate them.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it fails to address response heterogeneity across metastases. This study argues that spatiotemporal heterogeneity across metastases and organ-specific response is informative for drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients from five phase III clinical trials. Initially, a metric Gower distance was applied to quantify response heterogeneity across metastases. Next, the spatiotemporal response heterogeneity across anatomic sites, therapies, and KRAS mutation status was assessed and examined for its association with drug efficacy and long-term patient survival. The response of metastatic lesions broadly differed across anatomic sites and therapies. About 60% of patients had at least one lesion respond contrarily from total tumor size. High interlesion heterogeneity was associated with shorter progression-free survival and overall survival. Targeted therapies (bevacizumab or panitumumab) combined with standard chemotherapy reduced interlesion heterogeneity and elicited more favorable effects from liver lesions (P < 0.001) than chemotherapy alone. Moreover, the favorable responses in liver metastases (> 30% shrinkage) were associated with extended patient overall survival (P < 0.001), in contrast to lesions in the lungs and lymph nodes. Altogether, the spatiotemporal response heterogeneity across metastases informed drug efficacy and patient survival, which could improve the current methods for treatment evaluation and patient prognosis. Significance: These findings support the modification of RECIST criteria to include individual lesion response to improve assessments of drug efficacy.

Context Relinqing granules (RLQ) are being used alone or in combination with antibacterial drugs to treat urological disorders. Objective This study investigates the pharmacokinetics of RLQ in humans and the potential for RLQ-perpetrated interactions on transporters. Materials and methods Twelve healthy subjects (six women and six men) participated to compare single- and multiple-dose pharmacokinetics of RLQ. In the single-dose study, all 12 subjects received 8 g of RLQ orally. After a 7-d washout period, the subjects received 8 g of RLQ for seven consecutive days (t.i.d.) and then a single dose. Gallic acid (GA) and protocatechuic acid (PCA) in plasma and urine samples were analysed using LC-MS/MS. The transfected cells were used to study the inhibitory effect of GA (50–5000 μg/L) and PCA (10–1000 μg/L) on transporters OAT1, OAT3, OCT2, OATP1B1, P-gp and BCRP. Results GA and PCA were absorbed into the blood within 1 h after administration and rapidly eliminated with a half-life of less than 2 h. The mean peak concentrations of GA (102 and 176 μg/L) and PCA (4.54 and 7.58 μg/L) were lower in males than females, respectively. The 24 h urine recovery rates of GA and PCA were about 10% and 5%, respectively. The steady-state was reached in 7 d without accumulation. GA was a potent inhibitor of OAT1 (IC50 = 3.73 μM) and OAT3 (IC50 = 29.41 μM), but not OCT2, OATP1B1, P-gp or BCRP. Discussion and conclusions GA and PCA are recommended as PK-markers in RLQ-related pharmacokinetic and drug interaction studies. We should pay more attention to the potential for RLQ-perpetrated interactions on transporters.

BackgroundPembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and PD-L1 Tumor Proportion Score (TPS) ≥ 50%. Salvage therapies for patients who develop resistance in this setting are limited. Several retrospective studies have highlighted a subset of patients who benefit from pembrolizumab treatment beyond progression (TBP), but these results have yet to be validated in a rigorous prospective study.MethodsDue to the heterogeneity of within-patient responses to pembrolizumab, many patients can experience progressive disease while some of their lesions are still shrinking. We employed nonlinear mixed-effects modeling and virtual clinical trial simulations to evaluate the risk of unconditionally switching all progressors to salvage chemotherapy and to identify the subset of patients likely to benefit from pembrolizumab TBP. By using > 25,000 radiographic lesion diameter measurements from > 500 patients, we simulated individual lesion responses to pembrolizumab, chemotherapy, and pembrolizumab treatment failure followed by pembrolizumab TBP or salvage chemotherapy. We then assessed the benefit of pembrolizumab and chemotherapy in combination and explored a potential mechanism of intrapatient drug synergy called lesion-level independent action (LLIA).ResultsSwitching all progressors to salvage chemotherapy was suboptimal. Pembrolizumab TBP could extend progression-free survival (PFS) and control tumor burden more in nontarget progressors with fewer lesions prior to treatment initiation. Assuming LLIA but not patient-level independent action (PLIA) for combination therapy yielded longer PFS.ConclusionsPembrolizumab TBP may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC who experience nontarget progression, but prospective studies are warranted.

Background and PurposeDespite the promising results of therapeutic antibodies in engaging the immune system to eliminate malignant cells, many aspects of the complex interplay between immune cells and cancer cells during antibody therapy remain incompletely understood. This study aimed to develop a biosensor system that can evaluate direct cell-cell contact and interactions between immune effector and target cells induced by therapeutic antibodies in physiologically relevant environments.Experimental ApproachThe system uses two structural complementary luciferase units (SmBit and LgBit) expressed on the respective membranes of immune effector and target cells. Upon cell-cell contact, the two subunits form active NanoLuc, generating a luminescent signal, allowing for real-time monitoring of cell-cell interactions and quantitatively assessing the pharmacological effects of therapeutic antibodies.Key ResultsWe optimized the system to ensure selectivity by adjusting the spacer lengths between two luciferase units to minimize interference from nonspecific intercellular contact. The system was able to quantitatively and longitudinally monitor cell-cell interactions between NK and target cells induced by rituximab and between T and target cells induced by blinatumomab in a three-dimensional cell culture system. The observation that NK cells exhibited faster interactions with target cells compared to T cells is intriguing and suggests potential differences in the mechanisms or kinetics of cell-cell interactions between different types of effector cells and tumor cells.Conclusions and ImplicationsThe biosensor system has the potential for broad applications to optimize antibody pharmacology and efficacy in various therapeutic areas through a deeper understanding of antibody-mediated cell-cell interactions.What is already knownTherapeutic antibodies can activate the immune system to elicit cytotoxicity through inducing cell-cell interactions between immune and tumor cells.Approaches for longitudinally evaluating cell-cell interaction induced by therapeutic antibodies remain limited.What does this study addThis study develops a biosensor system for detecting cell-cell interactions induced by therapeutic antibodies in a longitudinal manner.The system is able to monitor cell-cell interactions between NK and target cells, as well as between T and target cells, in a three-dimensional cell culture system.What is the clinical significanceThe biosensor system has the potential for broad applications in the field of antibody pharmacology by providing a deeper understanding of antibody-mediated cell-cell interactions and their dynamics.

Effector T cells form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. With classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as a model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our model highlight the bone marrow may serve as a sanctuary site permitting tumor evolution and antigen escape. The models also suggest the optimal dosing regimens as a function of tumor growth and patient T cell abundance that confer adequate tumor control with minimal disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High intrapatient heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying intrapatient heterogeneity. However, our understanding of intrapatient heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and relapse during treatment, remains rudimentary. This study investigated intrapatient heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and relapse dynamics, metastatic lesions converged on four phenotypes that varied with anatomical site. Importantly, we found that organ-level relapse sequence was closely associated with patient survival, and that patients with the first relapses in the liver often had worse survival. In conclusion, our study provides insights into intrapatient response heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.

Predator-prey theory is commonly used to approximately describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor “shows” the body) and the T-cell receptor (TCR) repertoire (how well the body “sees” cancer cells). Importantly, both tumor and T cell populations are dynamic and in competition with each other and their fraternal lineages. In particular, the immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poorer survival and response to T-cell dependent immunotherapy in some tumor types, suggesting the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Whether between-lineage competition among T cells plays a role, and in what contexts, is unknown; moreover, longitudinal TCR profiling studies are expensive and logistically complex to conduct. In silico models may offer an inexpensive way to interrogate these phenomena ex ante and deepen our understanding of the tumor-immune axis. Here, we construct and calibrate a predator-prey-like model to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of neoantigen-specific T cell lineages and their consumption of both lineage-specific and shared, lineage-agnostic resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes as well as correlates and consequences of response to immunotherapy, including immunoediting. The model was also suitable for exploring treatment of tumors with varying growth and mutation rates.

Background: Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and PD-L1 Tumor Proportion Score (TPS) ≥ 50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest a subset of patients may benefit from pembrolizumab treatment beyond progression (TBP); these results have not been validated in a prospective study. Methods: We constructed digital twins of patients and enrolled them in virtual clinical trials to identify the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions are still shrinking. With > 25,000 radiographic lesion measurements from > 500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab TBP or salvage chemotherapy. We then assessed combination pembrolizumab and chemotherapy to explore a possible mechanism of intrapatient drug synergy called lesion-level independent action (LLIA).Results: Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials identified a subset of patients with fewer baseline lesions who experienced longer progression-free survival (PFS) and disease control on pembrolizumab TBP. Combination therapy under LLIA but not patient-level independent action (PLIA) yielded longer PFS.Conclusions: Pembrolizumab TBP may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted. LLIA may be an important consideration for future clinical investigation. Citation Format: Timothy Qi, Yanguang Cao. Virtual clinical trials: a tool for identifying patients who benefit from treatment beyond progression with pembrolizumab in NSCLC [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A21.

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79–1.49) and 1.54 (1.01–2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. Significance: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

The distribution of nanomedicines inside solid tumors is often restricted to perivascular areas, leaving most distal tumor cells out of reach. This partly explains modest patient benefit of many nanomedicines compared to their free-form counterparts. The objective for this study is to develop a mathematical model to quantitatively analyze this phenomenon and the influencing factors to such perivascular distribution and seek for effective strategies to alleviate this. A spatial tumor distribution model was firstly constructed to mimic the geometrical structure of tumor vessels and the surrounding tumor cells. This tumor model was further integrated with a systemic pharmacokinetics model for nanoparticles. A variety of factors on the tumor spatial distributions of nanomedicines were considered in the model. With the model, we quantified the effect of these influencing factors on tumor delivery efficacy (ID %), the magnitude of heterogeneous distribution (H index), and the effect of enhanced permeability and retention (EPR). In particularly, we compared the spatial distributions of the nanoparticles and the free payloads insides tumors. The model predicted high degrees of distributional heterogeneity for both nanoparticles and free payloads. The degree of heterogeneity and the influencing factors for free payloads were markedly different from those for nanoparticles. We found that nanoparticle diffusion coefficient was the most effective factor in reducing the nanoparticle H index but exerted moderate influence on the free payloads H index. The most effective factor in reducing the H index of free payload was payload diffusion coefficient. The factors that improved free payload distribution were closely associated with higher drug efficacy. In contrast, the factors that improved nanoparticle spatial distributions did not always confer improved anti-tumor efficacy of the delivered drug. These findings highlight the importance of assessing the heterogeneous free payload distribution in tumors for the development of effective nanomedicines.

Targeted suppression of autoimmune diseases without collateral suppression of normal immunity remains an elusive yet clinically important goal. Targeted blockade of the programmed death-1 receptor (PD-1) — an immune checkpoint factor expressed by activated T cells and B cells — is an efficacious therapy for potentiating immune activation against tumours. Here, we show that an immunotoxin consisting of an anti-PD-1 single-chain variable fragment, an albumin-binding domain and Pseudomonas exotoxin targeting PD-1-expressing cells selectively recognizes and induces the killing of the cells. Administration of the immunotoxin to mouse models of autoimmune diabetes delays disease onset, and its administration in mice paralyzed by experimental autoimmune encephalomyelitis ameliorates symptoms. In all mouse models, the immunotoxin reduced the numbers of PD-1-expressing cells, of total T cells and of cells of an autoreactive T cell clone found in inflamed organs, while maintaining active adaptive immunity, as evidenced by full-strength immune responses to vaccinations. The targeted depletion of PD-1-expressing cells contingent to the preservation of adaptive immunity might be effective in the treatment of a wide range of autoimmune diseases.

Background Investigating antiretroviral (ARV) penetration and pharmacology in lymph nodes is crucial to understanding mechanisms of HIV persistence in tissue, but sampling these tissues in humans is invasive and costly. Physiologically based pharmacokinetic (PBPK) modelling is a non-invasive solution for understanding lymph node penetration of ARVs across multiple species. Objectives To develop customized PBPK models with a novel lymph node compartment, and use these models to describe the distribution of three ARVs—tenofovir, emtricitabine and efavirenz—into the plasma and lymph nodes of non-human primates (NHPs) and humans. Materials and methods In this analysis, we utilized standard monkey and human PBPK models in PK-Sim, and added a novel lymph node compartment using MoBi. We used these models to describe the distribution of tenofovir, emtricitabine and efavirenz into NHP and human plasma and lymph nodes, and compared model-predicted versus observed AUC and Cmax. Results For all three ARVs, population simulations using the base and final models reasonably characterized observed plasma and tissue data in NHPs and humans, with predicted/observed AUC and Cmax ratios within 0.7–2.0. Conclusions Overall, our novel PBPK model provides a framework for understanding lymph node penetration of ARVs or future HIV cure therapies.

Considerable lesion-specific response heterogeneity exists in metastatic colorectal cancer patients, largely due to organ-specific ecological environments and evolutionary pressures. Metastatic lesions with poor response to therapy often become tumor sanctuary sites, leading to systemic resistance and tumor relapse. To map the lesion-specific response and relapse patterns, we investigated the longitudinal dynamics of individual lesions in metastatic colorectal cancer patients. Tumor longitudinal data in 4,308 colorectal cancer patients with 40,612 individual lesions were collected from eight Phase III trials in Project Data Sphere. First, tumor response dynamics (regression after treatment and progression upon resistance) were characterized using an empirical mathematical model. Next, tumor response time (when the lesion size decreases ≥20% from baseline) and relapse time (when the lesion size increases ≥30% from tumor nadir) were estimated for each individual lesion in patients being treated with bevacizumab, panitumumab, and/or chemotherapy. Random effect cox proportional models were applied to predict lesion-specific response and relapse probabilities and temporal sequence. We then took machine learning algorithm k-means to cluster patients based on their lesion relapse sequence. We found the response probabilities across organs are: Liver > Distal Lymph Nodes (LN) > Abdomen > Spleen > Lung > Regional LN > Adrenal > Muscle/Soft Tissue > Bone > Brain/CNS. Lesion relapse temporal sequence are: Brain/CNS > Liver > Adrenal > Muscle/Soft tissue > Abdomen > Bone > Spleen > Lung > Distal LN > Regional LN. Of note, lesions in the bone, brain, adrenal, and muscle/soft tissues often had low responses and high relapse probabilities, implying the greatest potential as tumor sanctuary sites. Liver, the most common metastatic organ in colorectal cancer, showed highest response rate but high relapse probabilities. Interestingly, the organ-specific response rate and relapse probabilities are respectively in line with drug distribution profiles and organ-specific immune landscape. Organ-specific relapse sequence in each patient is significantly correlated with patient long-term survival (p Citation Format: Jiawei Zhou, Quefeng Li, Amber Cipriani, Yanguang Cao. Mapping lesion specific response and relapse patterns in metastatic colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3789.

Neoantigen clonal architecture is known to influence the efficacy of immunotherapy. The immunodominance of clonal neoantigens over a canvas of heterogeneous subclonal neoantigens is particularly challenging to recapitulate in experimental models. We developed a computational model of neoantigen evolution that longitudinally tracks the “presentome” - that is, a cell’s aggregate neoantigen repertoire and HLA status - of simulated non-small cell lung cancer (NSCLC) prior to and during treatment with checkpoint blockade. Using neoantigen immunogenicity parameters from published reports, we first recapitulated the presentome of early-stage NSCLC. Upon simulating checkpoint blockade treatment of advanced NSCLC, we found salient correlates and consequences of response, including a profound clonal collapse among completely responding tumors. Our work could be adapted to longitudinally model the neoantigen architecture of other cancers and modalities of immunotherapy, ultimately informing cognizant strategies for future therapeutic development. Citation Format: Tim Qi, Benjamin Vincent, Yanguang Cao. Longitudinally modeling anti-neoantigen immunity under paradigms of immunodominance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2731.

SARS-CoV-2 utilizes the IMPα/β1 heterodimer to enter host cell nuclei after gaining cellular access through the ACE2 receptor. Ivermectin has shown antiviral activity by inhibiting the formation of the importin-α (IMPα) and IMPβ1 subunits as well as dissociating the IMPα/β1 heterodimer and has in vitro efficacy against SARS-CoV-2. Plasma and lung ivermectin concentrations vs. time profiles in cattle were used to determine the apparent plasma to lung tissue partition coefficient of ivermectin. This coefficient, together with a simulated geometric mean plasma profile of ivermectin from a published population pharmacokinetic model, was utilized to develop a minimal physiologically-based pharmacokinetic (mPBPK) model. The mPBPK model accurately described the simulated ivermectin plasma concentration profile in humans. The mPBPK model was also used to simulate human lung exposure to ivermectin after 12, 30, and 120 mg oral doses. The simulated ivermectin lung exposures reached a maximum concentration of 772 ng/mL, far less than the estimated 1750 ng/mL IC50 reported for ivermectin against SARS-CoV-2 in vitro. Further studies of ivermectin either reformulated for inhaled delivery or in combination with other antivirals with differing mechanisms of action is needed to assess its therapeutic potential. Fil: Jermain, Brian. University of North Carolina; Estados Unidos Fil: Hanafin, Patrick O.. University of North Carolina; Estados Unidos Fil: Cao, Yanguang. University of North Carolina; Estados Unidos Fil: Lifschitz, Adrian Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; Argentina Fil: Lanusse, Carlos Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; Argentina Fil: Rao, Gauri G.. University of North Carolina; Estados Unidos

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it neglects the response heterogeneity across metastases. This study argues that the spatiotemporal response heterogeneity across metastases informs drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients and examined their response heterogeneity. The response dynamics of metastatic lesions varied broadly across anatomical locations and therapies. High inter-lesion heterogeneity is associated with worse survival (p < 0.001), while targeted therapies (bevacizumab or panitumumab) reduced the inter-lesion heterogeneity (p < 0.05) and elicited more favorable effects on liver lesions (p < 0.001) than chemotherapy alone. The responses of liver lesions predicted patient survival more significantly than the lesions in the lungs and lymph nodes. Altogether, the high spatiotemporal heterogeneity across metastases should be integrated into current methods for treatment evaluation and patient prognosis.SignificanceThe spatiotemporal heterogeneity across metastases in response to first-line therapies in colorectal cancer is informative for drug efficacy and patient survival, particularly in targeted therapy. Our findings provide evidence to support the inclusion of individual lesion response in the RECIST to improve the assessment of drug efficacy and patient survival.

Antibodies have become an attractive class of therapeutic agents for solid tumors, mainly because of their high target selectivity and affinity. The target binding properties of antibodies are critical for their efficacy and toxicity. Our lab has developed a bioluminescence resonance energy transfer (BRET) imaging approach that directly supports the measurement of the binding dynamics between antibodies and their targets in the native tumor environment. In the present study, we have developed a spatially resolved computational model analyzing the longitudinal BRET imaging data of antibody–target binding and exploring the mechanisms of biphasic binding dynamics between a model antibody cetuximab and its target, the epidermal growth factor receptor (EGFR). The model suggested that cetuximab is bound differently to EGFR in the stroma-rich area than in stroma-poor regions, which was confirmed by immunofluorescence staining. Compared to the binding in vitro, cetuximab bound to EGFR to a “slower-but-tighter” degree in the living tumors. These findings have provided spatially resolved characterizations of antibody–target binding in living tumors and have yielded many mechanistic insights into the factors that affect antibody interactions with its targets and treatment efficacy.

Chimeric antigen receptor (CAR)-T cell therapy has achieved considerable success in treating B-cell hematologic malignancies. However, the challenges of extending CAR-T therapy to other tumor types, particularly solid tumors, remain appreciable. There are substantial variabilities in CAR-T cellular kinetics across CAR-designs, CAR-T products, dosing regimens, patient responses, disease types, tumor burdens, and lymphodepletion conditions. As a “living drug”, CAR-T cellular kinetics typically exhibit four distinct phases: distribution, expansion, contraction, and persistence. The cellular kinetics of CAR-T may correlate with patient responses, but which factors determine CAR-T cellular kinetics remain poorly defined. Herein, we developed a cellular kinetic model to retrospectively characterize CAR-T kinetics in 217 patients from 7 trials and compared CAR-T kinetics across response status, patient populations, and tumor types. Based on our analysis results, CAR-T cells exhibited a significantly higher cell proliferation rate and capacity but a lower contraction rate in patients who responded to treatment. CAR-T cells proliferate to a higher degree in hematologic malignancies than in solid tumors. Within the assessed dose ranges (107–109 cells), CAR-T doses were weakly correlated with CAR-T cellular kinetics and patient response status. In conclusion, the developed CAR-T cellular kinetic model adequately characterized the multiphasic CAR-T cellular kinetics and supported systematic evaluations of the potential influencing factors, which can have significant implications for the development of more effective CAR-T therapies.

Dose selection and confirmation are critical tasks in the development of therapeutic antibodies. These tasks could become particularly challenging in the absence of robust pharmacodynamics biomarkers or at very flat dose-response curves. Although much knowledge has been acquired in the past decade, it remains uncertain which factors are relevant and how to select doses more rationally. In this study, we developed a quantitative metric, Therapeutic Exposure Affinity Ratio (TEAR), to retrospectively evaluate up to 60 approved antibodies and their therapeutic doses (TDs), and systematically assessed the factors that are relevant to antibody TDs and dose selection patterns. This metric supported us to analyze many factors that are beyond antibody pharmacokinetics and target binding affinity. Our results challenged the traditional perceptions about the importance of target turnovers and target anatomical locations in the selection of TDs, highlighted the relevance of an overlooked factor, antibody mechanisms of action. Overall, this study provided insights into antibody dose selection and confirmation in the development of therapeutic antibodies.

Type 2 diabetes (T2DM) is a progressive disease, which is primarily characterized by a decline in β-cell function and worsening of insulin resistance. Unfortunately, most interventions (lifestyle, diet, and therapeutic agents) for T2DM only provide a transient restoration of β-cell function and the progression is inevitable once it starts. To understand the natural progression of T2DM, a mechanistic model was developed to quantitatively characterize the dynamic interactions among β-cell function, plasma fasting glucose (PFG), fasting insulin (FI), and the degree of insulin resistance, starting from an early stage of T2DM over up to 8 years. The model was validated using clinical data to optimize the disease parameters. The restoration and deterioration rates of β-cell function were both predicted as 84.5 %/year and 1.10 /year for early stages of T2DM. The model predicted a positive correlation between the initial level of β-cell function at diagnosis and its maximum restoration potential, underscoring the importance of early diagnosis and intervention. After the treatment, β-cell function could be temporarily restored within several months, which has a long-term benefit in glycemic control. The maximal tolerated PFG level that permits β-cell function restoration was predicted to be around 8.33 nM; and the temporal restoration of β-cell function would be unlikely at a PFG level above this threshold. The intrinsic deterioration rates of β-cell function and insulin resistance were both critical factors for long-term glycemic control. In conclusion, our model provides a quantitative analysis of the natural disease progression in T2DM and yields insights into factors that are critical for long-term glycemic control.