Sabatolimab (MBG453) model-informed drug development for dose selection in patients with myelodysplastic syndrome/acute myeloid leukemia and solid tumors.

Sabatolimab is a novel immunotherapy with immuno-myeloid activity that targets T-cell immunoglobulin domain and mucin domain-3 (TIM-3) on immune cells and leukemic blasts. It is being evaluated for the treatment of myeloid malignancies in the STIMULUS clinical trial program. The objective of this analysis was to support the sabatolimab dose-regimen selection in hematologic malignancies. A population pharmacokinetic (PopPK) model was fit to patients with solid tumors and hematologic malignancies, which included acute myeloid leukemia, myelodysplastic syndrome (including intermediate-, high-, and very high-risk per Revised International Prognostic Scoring System), and chronic myelomonocytic leukemia. The PopPK model, together with a predictive model of sabatolimab distribution to the bone marrow and binding to TIM-3 was used to predict membrane-bound TIM-3 bone marrow occupancy. In addition, the total soluble TIM-3 (sTIM-3) kinetics and the pharmacokinetic (PK) exposure-response relationship in patients with hematologic malignancies were examined. At intravenous doses above 240 mg Q2w and 800 mg Q4w, we observed linear PK, a plateau in the accumulation of total sTIM-3, and a flat exposure-response relationship for both safety and efficacy. In addition, the model predicted membrane-bound TIM-3 occupancy in the bone marrow was above 95% in over 95% of patients. Therefore, these results support the selection of the 400 mg Q2w and 800 mg Q4w dosing regimens for the STIMULUS clinical trial program.

Current practices for QSP model assessment: an IQ consortium survey.

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.

Estimating drug potency in the competitive target mediated drug disposition (TMDD) system when the endogenous ligand is included.

Predictions for target engagement are often used to guide drug development. In particular, when selecting the recommended phase 2 dose of a drug that is very safe, and where good biomarkers for response may not exist (e.g. in immuno-oncology), a receptor occupancy prediction could even be the main determinant in justifying the approved dose, as was the case for atezolizumab. The underlying assumption in these models is that when the drug binds its target, it disrupts the interaction between the target and its endogenous ligand, thereby disrupting downstream signaling. However, the interaction between the target and its endogenous binding partner is almost never included in the model. In this work, we take a deeper look at the in vivo system where a drug binds to its target and disrupts the target's interaction with an endogenous ligand. We derive two simple steady state inhibition metrics (SSIMs) for the system, which provides intuition for when the competition between drug and endogenous ligand should be taken into account for guiding drug development.

Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches.

Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.

Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems.

Guiding the dose selection for monoclonal antibody oncology drugs is often done using methods for predicting the receptor occupancy of the drug in the tumor. In this manuscript, previous work on characterizing target inhibition at steady state using the AFIR metric (Stein and Ramakrishna in CPT Pharmacomet Syst Pharmacol 6(4):258-266, 2017) is extended to include a "target-tissue" compartment and the shedding of membrane-bound targets. A new potency metric average free tissue target to initial target ratio (AFTIR) at steady state is derived, and it depends on only four key quantities: the equilibrium binding constant, the fold-change in target expression at steady state after binding to drug, the biodistribution of target from circulation to target tissue, and the average drug concentration in circulation. The AFTIR metric is useful for guiding dose selection, for efficiently performing sensitivity analyses, and for building intuition for more complex target mediated drug disposition models. In particular, reducing the complex, physiological model to four key parameters needed to predict target inhibition helps to highlight specific parameters that are the most important to estimate in future experiments to guide drug development.

Tisagenlecleucel Model-Based Cellular Kinetic Analysis of Chimeric Antigen Receptor-T Cells.

Tisagenlecleucel is a chimeric antigen receptor-T cell therapy that facilitates the killing of CD19+ B cells. A model was developed for the kinetics of tisagenlecleucel and the impact of therapies for treating cytokine release syndrome (tocilizumab and corticosteroids) on expansion. Data from two phase II studies in pediatric and young adult relapsed/refractory B cell acute lymphoblastic leukemia were pooled to evaluate this model and evaluate extrinsic and intrinsic factors that may impact the extent of tisagenlecleucel expansion. The doubling time, initial decline half-life, and terminal half-life for tisagenlecleucel were 0.78, 4.3, and 220 days, respectively. No impact of tocilizumab or corticosteroids on the expansion rate was observed. This work represents the first mixed-effect model-based analysis of chimeric antigen receptor-T cell therapy and may be clinically impactful as future studies examine prophylactic interventions in patients at risk of higher grade cytokine release syndrome and the effects of these interventions on chimeric antigen receptor-T cell expansion.

Clinical Pharmacology of Tisagenlecleucel in B-cell Acute Lymphoblastic Leukemia.

PURPOSE: Tisagenlecleucel is an anti-CD19 chimeric antigen receptor (CAR19) T-cell therapy approved for the treatment of children and young adults with relapsed/refractory (r/r) B-cell acute lymphoblastic leukemia (B-ALL). PATIENTS AND METHODS: We evaluated the cellular kinetics of tisagenlecleucel, the effect of patient factors, humoral immunogenicity, and manufacturing attributes on its kinetics, and exposure-response analysis for efficacy, safety and pharmacodynamic endpoints in 79 patients across two studies in pediatric B-ALL (ELIANA and ENSIGN). RESULTS: Using quantitative polymerase chain reaction to quantify levels of tisagenlecleucel transgene, responders (N = 62) had ≈2-fold higher tisagenlecleucel expansion in peripheral blood than nonresponders (N = 8; 74% and 104% higher geometric mean Cmax and AUC0-28d, respectively) with persistence measurable beyond 2 years in responding patients. Cmax increased with occurrence and severity of cytokine release syndrome (CRS). Tisagenlecleucel continued to expand and persist following tocilizumab, used to manage CRS. Patients with B-cell recovery within 6 months had earlier loss of the transgene compared with patients with sustained clinical response. Clinical responses were seen across the entire dose range evaluated (patients ≤50 kg: 0.2 to 5.0 × 106/kg; patients >50 kg: 0.1 to 2.5 × 108 CAR-positive viable T cells) with no relationship between dose and safety. Neither preexisting nor treatment-induced antimurine CAR19 antibodies affected the persistence or clinical response. CONCLUSIONS: Response to tisagenlecleucel was associated with increased expansion across a wide dose range. These results highlight the importance of cellular kinetics in understanding determinants of response to chimeric antigen receptor T-cell therapy.

Predicting the Onset of Nonlinear Pharmacokinetics.

When analyzing the pharmacokinetics (PK) of drugs, one is often faced with concentration C vs. time curves, which display a sharp transition at a critical concentration Ccrit . For C > Ccrit , the curve displays linear clearance and for C < Ccrit clearance increases in a nonlinear manner as C decreases. Often, it is important to choose a high enough dose such that PK remains linear in order to help ensure that continuous target engagement is achieved throughout the duration of therapy. In this article, we derive a simple expression for Ccrit for models involving linear and nonlinear (saturable) clearance, such as Michaelis-Menten and target-mediated drug disposition (TMDD) models. Study Highlights.

Benchmarking QSP Models Against Simple Models: A Path to Improved Comprehension and Predictive Performance.

Quantitative Systems Pharmacology (QSP) models provide a means of integrating knowledge into a quantitative framework and, ideally, this integration leads to a better understanding of biology and better predictions of new experiments and clinical trials. In practice, these goals may be compromised by model complexity and uncertainty. To address these problems, we recommend that the predictive performance of QSP models be assessed through comparison with simpler models developed specifically for this purpose.

Rapid initial decline in BCR-ABL1 is associated with superior responses to second-line nilotinib in patients with chronic-phase chronic myeloid leukemia.

BACKGROUND: We evaluated BCR-ABL1 kinetics in patients treated with nilotinib and analyzed whether a dynamic model of changes in BCR-ABL1 levels over time could be used to predict long-term responses. METHODS: Patients from the nilotinib registration trial (CAMN107A2101; registered at http://www.clinicaltrials.gov as NCT00109707) who had imatinib-resistant or -intolerant Philadelphia chromosome-positive (Ph+) chronic myeloid leukemia (CML) in chronic phase (CP) or accelerated phase with BCR-ABL1 > 10% (on the international scale [IS]) at baseline and, in the first 6 months, had at least three BCR-ABL1 transcript measurements and an average daily dose of at least 720 mg were included in this analysis (N = 123). RESULTS: More than half of patients (65/123; 53%) had a slow monophasic response and the remainder (58/123; 47%) had a biphasic response, in which patients had a rapid initial decrease in BCR-ABL1 transcripts followed by a more gradual response. The biphasic response type strongly correlated with improved event-free survival (EFS). Data in the first 6 months of follow-up were sufficient to predict EFS at 24 months. CONCLUSIONS: Unlike newly diagnosed patients with Ph+ CML-CP-in whom the majority had a biphasic response-approximately half of patients with imatinib-resistant or -intolerant CML had a slower, monophasic response. Second-line patients who did have a biphasic response had an EFS outlook similar to that of newly diagnosed patients treated with imatinib. Our model was comparable to using BCR-ABL1 (IS) ≤ 10% at 6 months as a threshold for predicting EFS.

BCR-ABL transcript dynamics support the hypothesis that leukemic stem cells are reduced during imatinib treatment.

PURPOSE: Imatinib induces a durable response in most patients with Philadelphia chromosome-positive chronic myeloid leukemia, but it is currently unclear whether imatinib reduces the leukemic stem cell (LSC) burden, which may be an important step toward enabling safe discontinuation of therapy. In this article, we use mathematical models of BCR-ABL levels to make inferences on the dynamics of LSCs. EXPERIMENTAL DESIGN: Patients with at least 1 BCR-ABL transcript measurement on imatinib were included (N = 477). Maximum likelihood methods were used to test 3 potential hypotheses of the dynamics of BCR-ABL transcripts on imatinib therapy: (i) monoexponential, in which there is little, if any, decline in BCR-ABL transcripts; (ii) biexponential, in which patients have a rapid initial decrease in BCR-ABL transcripts followed by a more gradual response; and (iii) triexponential, in which patients first exhibit a biphasic decline but then have a third phase when BCR-ABL transcripts increase rapidly. RESULTS: We found that most patients treated with imatinib exhibit a biphasic decrease in BCR-ABL transcript levels, with a rapid decrease during the first few months of treatment, followed by a more gradual decrease that often continues over many years. CONCLUSIONS: We show that the only hypothesis consistent with current data on progenitor cell turnover and with the long-term, gradual decrease in the BCR-ABL levels seen in most patients is that these patients exhibit a continual, gradual reduction of the LSCs. This observation may explain the ability to discontinue imatinib therapy without relapse in some cases.

Dissecting the serotonergic food signal stimulating sensory-mediated aversive behavior in C. elegans.

Nutritional state often modulates olfaction and in Caenorhabditis elegans food stimulates aversive responses mediated by the nociceptive ASH sensory neurons. In the present study, we have characterized the role of key serotonergic neurons that differentially modulate aversive behavior in response to changing nutritional status. The serotonergic NSM and ADF neurons play antagonistic roles in food stimulation. NSM 5-HT activates SER-5 on the ASHs and SER-1 on the RIA interneurons and stimulates aversive responses, suggesting that food-dependent serotonergic stimulation involves local changes in 5-HT levels mediated by extrasynaptic 5-HT receptors. In contrast, ADF 5-HT activates SER-1 on the octopaminergic RIC interneurons to inhibit food-stimulation, suggesting neuron-specific stimulatory and inhibitory roles for SER-1 signaling. Both the NSMs and ADFs express INS-1, an insulin-like peptide, that appears to cell autonomously inhibit serotonergic signaling. Food also modulates directional decisions after reversal is complete, through the same serotonergic neurons and receptors involved in the initiation of reversal, and the decision to continue forward or change direction after reversal is dictated entirely by nutritional state. These results highlight the complexity of the "food signal" and serotonergic signaling in the modulation of sensory-mediated aversive behaviors.

An algorithm for extracting the network geometry of three-dimensional collagen gels.

The geometric structure of a biopolymer network impacts its mechanical and biological properties. In this paper, we develop an algorithm for extracting the network architecture of three-dimensional (3d) fluorescently labeled collagen gels, building on the initial work of Wu et al., (2003). Using artificially generated images, the network extraction algorithm is then validated for its ability to reconstruct the correct bulk properties of the network, including fiber length, persistence length, cross-link density, and shear modulus.

Lithium inhibits invasion of glioma cells; possible involvement of glycogen synthase kinase-3.

Therapies targeting glioma cells that diffusely infiltrate normal brain are highly sought after. Our aim was to identify novel approaches to this problem using glioma spheroid migration assays. Lithium, a currently approved drug for the treatment of bipolar illnesses, has not been previously examined in the context of glioma migration. We found that lithium treatment potently blocked glioma cell migration in spheroid, wound-healing, and brain slice assays. The effects observed were dose dependent and reversible, and worked using every glioma cell line tested. In addition, there was little effect on cell viability at lithium concentrations that inhibit migration, showing that this is a specific effect. Lithium treatment was associated with a marked change in cell morphology, with cells retracting the long extensions at their leading edge. Examination of known targets of lithium showed that inositol monophosphatase inhibition had no effect on glioma migration, whereas inhibition of glycogen synthase kinase-3 (GSK-3) did. This suggested that the effects of lithium on glioma cell migration could possibly be mediated through GSK-3. Specific pharmacologic GSK-3 inhibitors and siRNA knockdown of GSK-3alpha or GSK-3beta isoforms both reduced cell motility. These data outline previously unidentified pathways and inhibitors that may be useful for the development of novel anti-invasive therapeutics for the treatment of brain tumors.

Estimating the cell density and invasive radius of three-dimensional glioblastoma tumor spheroids grown in vitro.

To gain insight into brain tumor invasion, experiments are conducted on multicellular tumor spheroids grown in collagen gel. Typically, a radius of invasion is reported, which is obtained by human measurement. We present a simple, heuristic algorithm for automated invasive radii estimation (AIRE) that uses local fluctuations of the image intensity. We then derive an analytical expression relating the image graininess to the cell density for a model imaging system. The result agrees with the experiment up to a multiplicative constant and thus describes a novel method for estimating the cell density from bright-field images.

A mathematical model of glioblastoma tumor spheroid invasion in a three-dimensional in vitro experiment.

Glioblastoma, the most malignant form of brain cancer, is responsible for 23% of primary brain tumors and has extremely poor outcome. Confounding the clinical management of glioblastomas is the extreme local invasiveness of these cancer cells. The mechanisms that govern invasion are poorly understood. To gain insight into glioblastoma invasion, we conducted experiments on the patterns of growth and dispersion of U87 glioblastoma tumor spheroids in a three-dimensional collagen gel. We studied two different cell lines, one with a mutation to the EGFR (U87DeltaEGFR) that is associated with increased malignancy, and one with an endogenous (wild-type) receptor (U87WT). We developed a continuum mathematical model of the dispersion behaviors with the aim of identifying and characterizing discrete cellular mechanisms underlying invasive cell motility. The mathematical model quantitatively reproduces the experimental data, and indicates that the U87WT invasive cells have a stronger directional motility bias away from the spheroid center as well as a faster rate of cell shedding compared to the U87DeltaEGFR cells. The model suggests that differences in tumor cell dispersion may be due to differences in the chemical factors produced by cells, differences in how the two cell lines remodel the gel, or different cell-cell adhesion characteristics.