In situ clinical measurement of receptor occupancy (RO) is challenging, particularly for solid tumors, necessitating the use of mathematical models that predict tumor receptor occupancy to guide dose decisions. A potency metric, average free tissue target to initial target ratio (AFTIR), was previously described based on a mechanistic compartmental model and is informative for near-saturating dose regimens. However, the metric fails at clinically relevant subsaturating antibody doses, as compartmental models cannot capture the spatial heterogeneity of distribution faced by some antibodies in solid tumors. Here we employ a partial differential equation (PDE) Krogh cylinder model to simulate spatiotemporal receptor occupancy and derive an analytical solution, a mechanistically weighted global AFTIR, that can better predict receptor occupancy regardless of dosing regimen. In addition to the four key parameters previously identified, a fifth key parameter, the absolute receptor density (targets/cell), is incorporated into the mechanistic AFTIR metric. Receptor density can influence equilibrium intratumoral drug concentration relative to whether the dose is saturating or not, thereby influencing the tumor penetration depth of the antibody. We derive mechanistic RO predictions based on distinct patterns of antibody tumor penetration, presented as a global AFTIR metric guided by a Thiele Modulus and a local saturation potential (drug equivalent of binding potential for positron emissions tomography imaging) and validate the results using rigorous global and local sensitivity analysis. This generalized AFTIR serves as a more accurate analytical metric to aid clinical dose decisions and rational design of antibody-based therapeutics without the need for extensive PDE simulations. SIGNIFICANCE STATEMENT Determining antibody-receptor occupancy (RO) is critical for dosing decisions in pharmaceutical development, but direct clinical measurement of RO is often challenging and invasive, particularly for solid tumors. Significant efforts have been made to develop mathematical models and simplified analytical metrics of RO, but these often require complex computer simulations. Here we present a mathematically rigorous but simplified analytical model to accurately predict RO across a range of affinities, doses, drug, and tumor properties.

CCR Translation for This Article from BCR–ABL Transcript Dynamics Support the Hypothesis That Leukemic Stem Cells Are Reduced during Imatinib Treatment

3125 Background: MAP2K1 (MEK1) mutations are potentially actionable driver mutations in cancer. MAP2K1 mutations can be classified into 3 classes according to molecular characteristics. The efficacy of MAPK inhibitors (MAPKi) for the treatment of MAP2K1 mutant tumors is not well understood. We sought to characterize the genomic and clinical landscape of MAP2K1 mutant tumors, and to evaluate the relationship between MAP2K1 mutation class and clinical activity of MAPKi in patients with MAP2K1 mutant metastatic solid tumors. Methods: We interrogated AACR GENIE (v13) to identify all tumors with Class 1/2/3 MAP2K1 mutant solid tumors. We performed a systematic review and meta-analysis of individual patient data from patients with MAP2K1 mutant cancer published between 2010-22. Key inclusion criteria were: MAP2K1 mutation, solid tumor, metastatic disease, treatment with MAPKi and available treatment response data. The primary endpoint was progression-free survival (PFS) and the secondary endpoints were overall response rate (RR) and duration of response (DOR). Chi-squared and Log-Rank tests were used to evaluate statistical significance of differences between groups. Results: MAP2K1 driver mutations were present in 917/167,423 (0.5%) tumors in the AACR GENIE dataset. MAP2K1 mutants were most commonly identified in melanoma, colorectal (CRC) and non-small cell lung cancer (NSCLC). In solid tumors, Class 2 mutations were the most prevalent (n=310, 63%) followed by Class 1 (n=119, 24%) and Class 3 (n=66, 13%). Co-occurring MAPK pathway activating mutations (KRAS, NRAS, HRAS, NF1, BRAF, RAF1, or EGFR) were significantly more likely (P<0.0001) to occur in Class 1 (82.3%), versus Class 2 (30.9%) or Class 3 (10.6%) MAP2K1 mutant tumors. We identified 55 patients with MAP2K1 mutant tumors who received MAPKi (n=16/30/6/3 for Class 1/2/3/unclassified, respectively). Of these, (n=22, 18, 12, 3) had melanoma, CRC, NSCLC, or other cancers, respectively. Patients were treated with BRAFi (n=12), MEKi (n=24), BRAF+MEKi (n=2), ERKi (n=1) or EGFRi (n=16). Co-occurring MAPK pathway mutations were present in 51% of tumors. In the entire cohort, the RR was 24% and median PFS was 3.3 months. The RR did not differ according to mutation class, cancer type or MAPKi regimen. However, patients with Class 2 mutations experienced longer PFS (4.0 months) and DOR (23.8 months) compared to patients with Class 1, 3 or unclassified MAP2K1 mutations (PFS 3.0 months, P=0.035; DOR 4.2 months, P=0.04). Conclusions: Class 2 MAP2K1 mutations are RAF-regulated oncogenic mutations with a relatively low incidence of co-occurring MAPK pathway activating mutations. Some patients with Class 2 MAP2K1 mutations may derive durable therapeutic benefit from MAPKi. Prospective clinical studies with MAPK inhibitors are warranted in patients with MAP2K1-mutated metastatic cancer.

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.

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.

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.

Objectives: Cellular kinetic (CK) measurement of CAR-T cell expansion in-vivo by quantitative polymerase chain reaction (qPCR) has been measured in units of transgene copy number/μg of DNA. We propose a formula to convert the qPCR, and flow cytometry (FC) measurements to interpretable numbers of CAR T-cells/μL blood. Further, after the conversion CK parameters were correlated with efficacy/safety endpoints. Methods: CK data measured using both qPCR and FC assays were utilized from the ELIANA trial in pediatric and young adult patients with relapsed and refractory acute lymphoblastic leukemia (pALL). qPCR measures the presence of CAR transgene in cells with units of copies of CAR-DNA/μg of genomic DNA, while FC quantifies surface expression of CAR T-cells as the % of either T-cells or white blood cells (WBCs) that express CAR. Neither measurement accounts for the typical significant increase in WBCs following CAR-T infusion. We propose equations for converting FC (Eq1) or qPCR (Eq2) into concentration of CAR-cells/μL of blood, using WBC counts from the complete blood count. For qPCR, the equation relies on 3 additional parameters M·F/N: amount of DNA/WBC (M), average number of copies of CAR-DNA/CTL019 cell (N), and fraction of cells with CAR-DNA that express CAR receptor (F). To estimate M·F/N, we performed regression of the cells/μL estimate from FC vs qPCR. We fit the CK model [1] to obtain the model parameters. Finally, CK parameters with safety/efficacy were correlated to compare the converted estimates to the native CK units. • (CD3+CAR+cells)/μl blood= WBC/μl blood × (CD3+CAR+cells)/WBC (1) • (CAR+cells)/μl blood=WBC/μl blood × CAR DNA copies/μg DNA × M μg DNA/WBC × 1 CAR cell/N CAR DNA copies×F (2) Results: There was high correlation between FC and qPCR estimates of CAR-T in the blood of pALL patients (r2=0.775). The M·F/N value derived based on FC and qPCR results was estimated as 2.68e-6μg DNA/CAR copies. Assuming M=6.6e-6μg DNA/WBC [2] and F=1, this predicts that N=2.46 CAR copies/CAR-T cell. There was also high correlation between the copies/μg and cells/μL estimates using qPCR (r2=0.752). The relationship between CK parameters and safety/efficacy endpoints was not improved when cells/μL was used. This can be attributed to high correlation between these metrics. Using the CK model, 11x greater fold expansion was predicted using the cells/μL estimate compared to copies/μg. This is because cells/μL estimate accounts the expansion of both CAR-T and WBC numbers following lymphodepletion. Conclusions: The conversion of CK into cells/μL allows for a physiological interpretation of CK data with a high correlation between the cells/μL and copies/μg indicating either metric can be used to predict safety/efficacy. References: 1 Stein M et al. CPT: Pharmacometrics & Systems Pharmacology(2019) 2 Gillooly JF et al. Cold Spring Harbor Perspectives in Biology vol.7,7 a019091(2015) Citation Format: Anwesha Chaudhury, Andrew Stein, Stephan Grupp, John Levine, Michael Pulsipher, G Doug Myers, Edward Waldron, Xu Zhu, Fraser McBlane, Rakesh Awasthi, Edmund K. Waller. Conversion of cellular kinetic data for chimeric antigen receptor T-cell therapy (CAR-T) into interpretable units [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 509.

Background: Extended T-cell culture periods in vitro deplete the CAR-T final product of naive and stem cell memory T-cell (T scm) subpopulations that are associated with improved antitumor efficacy. YTB323 is an autologous CD19-directed CAR-T cell therapy with dramatically simplified manufacturing, which eliminates complexities such as long culture periods. This improved T-Charge™ process preserves T-cell stemness, an important characteristic closely tied to therapeutic potential, which leads to enhanced expansion ability and greater antitumor activity of CAR-T cells. Methods: The new T-Charge TM manufacturing platform, which reduces ex vivo culture time to about 24 hours and takes <2 days to manufacture the final product, was evaluated in a preclinical setting. T cells were enriched from healthy donor leukapheresis, followed by activation and transduction with a lentiviral vector encoding for the same CAR used for tisagenlecleucel. After ≈24 hours of culture, cells were harvested, washed, and formulated (YTB323). In parallel, CAR-T cells (CTL*019) were generated using a traditional ex vivo expansion CAR-T manufacturing protocol (TM process) from the same healthy donor T cells and identical lentiviral vector. Post manufacturing, CAR-T products were assessed in T-cell functional assays in vitro and in vivo, in immunodeficient NSG mice (NOD-scid IL2Rg-null) inoculated with a pre-B-ALL cell line (NALM6) or a DLBCL cell line (TMD-8) to evaluate antitumor activity and CAR-T expansion. Initial data from the dose escalation portion of the Phase 1 study will be reported separately. Results: YTB323 CAR-T products, generated via this novel expansionless manufacturing process, retained the immunophenotype of the input leukapheresis; specifically, naive/T scm cells (CD45RO -/CCR7 +) were retained as shown by flow cytometry. In contrast, the TM process with ex vivo expansion generated a final product consisting mainly of central memory T cells (T cm) (CD45RO +/CCR7 +) (Fig A). Further evidence to support the preservation of the initial phenotype is illustrated by bulk and single-cell RNA sequencing experiments, comparing leukapheresis and final products from CAR-Ts generated using the T-Charge™ and TM protocols. YTB323 CAR-T cell potency was assessed in vitro using a cytokine secretion assay and a tumor repeat stimulation assay, designed to test the persistence and exhaustion of the cell product. YTB323 T cells exhibited 10- to 17-fold higher levels of IL-2 and IFN-γ secretion upon CD19-specific activation compared with CTL*019. Moreover, YTB323 cells were able to control the tumor at a 30-fold lower Effector:Tumor cell ratio and for a minimum of 7 more stimulations in the repeat stimulation assay. Both assays clearly demonstrated enhanced potency of the YTB323 CAR-T cells in vitro. The ultimate preclinical assessment of the YTB323 cell potency was through comparison with CTL*019 regarding in vivo expansion and antitumor efficacy against B-cell tumors in immunodeficient NSG mouse models at multiple doses. Expansion of CD3+/CAR+ T-cells in blood was analyzed weekly by flow cytometry for up to 4 weeks postinfusion. Dose-dependent expansion (C max and AUC 0-21d) was observed for both YTB323 and CTL*019. C max was ≈40-times higher and AUC 0-21d was ≈33-times higher for YTB323 compared with CTL*019 across multiple doses. Delayed peak expansion (T max) of YTB323 by at least 1 week compared with CTL*019 was observed, supporting that increased expansion was driven by the less differentiated T-cell phenotype of YTB323. YTB323 controlled NALM6 B-ALL tumor growth at a lower dose of 0.1×10 6 CAR+ cells compared to 0.5×10 6 CAR+ cells required for CTL*019 (Fig B). In the DLBCL model TMD-8, only YTB323 was able to control the tumors while CTL*019 led to tumor progression at the respective dose groups. This ability of YTB323 cells to control the tumor at lower doses confirms their robustness and potency. Conclusions: The novel manufacturing platform T-Charge™ used for YTB323 is simplified, shortened, and expansionless. It thereby preserves T-cell stemness, associated with improved in vivo CAR-T expansion and antitumor efficacy. Compared to approved CAR-T therapies, YTB323 has the potential to achieve higher clinical efficacy at its respective lower doses. T-Charge™ is aiming to substantially revolutionize CAR-T manufacturing, with concomitant higher likelihood of long-term deep responses. Figure 1 Figure 1. Engels: Novartis: Current Employment, Current equity holder in publicly-traded company. Zhu: Novartis: Current Employment, Current equity holder in publicly-traded company. Yang: Novartis: Current Employment, Patents & Royalties. Price: Novartis: Current Employment. Sohoni: Novartis: Current Employment. Stein: Novartis: Current Employment. Parent: Novartis: Ended employment in the past 24 months; iVexSol, Inc: Current Employment. Greene: iVexSol, Inc: Current Employment, Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Niederst: Novartis: Current Employment, Current equity holder in publicly-traded company. Whalen: Novartis: Current Employment. Orlando: Novartis: Current Employment. Treanor: Novartis: Current Employment, Current holder of individual stocks in a privately-held company, Divested equity in a private or publicly-traded company in the past 24 months, Patents & Royalties: no royalties as company-held patents. Brogdon: Novartis Institutes for Biomedical Research: Current Employment.

Context: Sabatolimab is an investigational immuno-myeloid therapy targeting TIM-3. The STIMULUS clinical trial program has been designed to evaluate sabatolimab in multiple phase 1–3 trials in patients with MDS and AML. Here, we report PK and clinical data supporting sabatolimab doses being evaluated in the STIMULUS program. Methods: PK was evaluated in patients with advanced solid tumors (NCT02608268), or high/very-high risk MDS, or AML who were ineligible for intensive chemotherapy (NCT03066648). Main Outcome Measures: Total sabatolimab serum concentration was used in population PK modeling to simulate average (Cavg), maximal (Cmax), and trough (Ctrough) concentrations at steady-state. Total serum soluble TIM-3 was measured, and simulation was used to predict membrane-bound TIM-3 occupancy in the bone marrow (BM). PK exposure-response analysis (data cutoff 27 Nov 2019) and assessment of clinical safety/efficacy by dose (data cutoff 25 Jun 2020) were conducted. Results: Sabatolimab PK was similar for patients with solid tumors (n=252) and higher-risk MDS and AML (n=155); no drug–drug interactions were observed for any combinations. Among sabatolimab+HMA regimens, sabatolimab 400 mg Q2W had the highest Ctrough at steady-state, and 800 mg was predicted to be an equivalent Q4W dosing regimen. Both doses had similar steady-state Cavg and similarly high occupancy rates for membrane-bound TIM-3 in the BM (>95% in ≥95% of patients with higher-risk MDS/AML), suggesting similarly high levels of TIM-3 engagement. There was no relationship between steady-state Cmax or Cavg quartiles and the incidence of treatment-related AEs. Exposure-efficacy analysis showed no clear relationship between steady-state Ctrough or Cavg and percent BM blast reduction or clinical benefit (CR/mCR/CRi/PR). Sabatolimab+HMA was safe and well-tolerated, with a low discontinuation rate due to AEs (3.4% [4/116]). Rates of most common grade ≥3 treatment-emergent AEs did not appear to be dose-dependent. Conclusions: Sabatolimab 400 mg Q2W was predicted to have the highest steady-state Ctrough and TIM-3 occupancy rate when combined with HMA; 800 mg was predicted to be an equivalent dosing regimen. No clear relationship was seen between sabatolimab dose or steady-state exposure and safety/efficacy. These results support the clinical development of the sabatolimab 400 mg Q2W and 800 mg Q4W dosing regimens. Sabatolimab is an investigational immuno-myeloid therapy targeting TIM-3. The STIMULUS clinical trial program has been designed to evaluate sabatolimab in multiple phase 1–3 trials in patients with MDS and AML. Here, we report PK and clinical data supporting sabatolimab doses being evaluated in the STIMULUS program. PK was evaluated in patients with advanced solid tumors (NCT02608268), or high/very-high risk MDS, or AML who were ineligible for intensive chemotherapy (NCT03066648). Total sabatolimab serum concentration was used in population PK modeling to simulate average (Cavg), maximal (Cmax), and trough (Ctrough) concentrations at steady-state. Total serum soluble TIM-3 was measured, and simulation was used to predict membrane-bound TIM-3 occupancy in the bone marrow (BM). PK exposure-response analysis (data cutoff 27 Nov 2019) and assessment of clinical safety/efficacy by dose (data cutoff 25 Jun 2020) were conducted. Sabatolimab PK was similar for patients with solid tumors (n=252) and higher-risk MDS and AML (n=155); no drug–drug interactions were observed for any combinations. Among sabatolimab+HMA regimens, sabatolimab 400 mg Q2W had the highest Ctrough at steady-state, and 800 mg was predicted to be an equivalent Q4W dosing regimen. Both doses had similar steady-state Cavg and similarly high occupancy rates for membrane-bound TIM-3 in the BM (>95% in ≥95% of patients with higher-risk MDS/AML), suggesting similarly high levels of TIM-3 engagement. There was no relationship between steady-state Cmax or Cavg quartiles and the incidence of treatment-related AEs. Exposure-efficacy analysis showed no clear relationship between steady-state Ctrough or Cavg and percent BM blast reduction or clinical benefit (CR/mCR/CRi/PR). Sabatolimab+HMA was safe and well-tolerated, with a low discontinuation rate due to AEs (3.4% [4/116]). Rates of most common grade ≥3 treatment-emergent AEs did not appear to be dose-dependent. Sabatolimab 400 mg Q2W was predicted to have the highest steady-state Ctrough and TIM-3 occupancy rate when combined with HMA; 800 mg was predicted to be an equivalent dosing regimen. No clear relationship was seen between sabatolimab dose or steady-state exposure and safety/efficacy. These results support the clinical development of the sabatolimab 400 mg Q2W and 800 mg Q4W dosing regimens.

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.

Background: Sabatolimab (MBG453) is a high-affinity, humanized, IgG4 (S228P) antibody targeting TIM-3, an inhibitory receptor expressed on multiple immune cells and on leukemic stem/progenitor cells and blasts, but not on normal hematopoietic stem cells. Sabatolimab is being evaluated for treatment of patients (pts) with intermediate to very high risk MDS or AML in the STIMULUS clinical trial program. Here we report PK and clinical data supporting sabatolimab doses being evaluated in the STIMULUS program. Methods: PK analyses were done in a ph 1-1b/2 study in pts with adv solid tumors (NCT02608268) and a ph 1b study in pts with high/very high risk MDS (HR-MDS) or AML who were ineligible for intensive chemotherapy (NCT03066648). Pts with solid tumors received IV sabatolimab 80-1200 mg Q2W/Q4W or sabatolimab 20-800 mg Q2W/80-1200 mg Q4W + spartalizumab (PD-1 inhibitor). Based on findings in solid tumors, pts with HR-MDS/AML received IV sabatolimab 160-1200 mg Q2W/800 mg Q4W in treatment arms including sabatolimab monotherapy, + hypomethylating agent (HMA; decitabine [Dec] or azacitidine), and + spartalizumab (± Dec). Total sabatolimab serum concentration was used in population PK (popPK) modeling to simulate average (Cavg), maximal (Cmax), and trough (Ctrough) concentrations at steady state. Total serum soluble TIM-3 was measured and simulation was used to predict membrane-bound TIM-3 occupancy in the bone marrow (BM). PK exposure-response analysis (data cutoff 27 Nov 2019) and assessment of clinical safety/efficacy by dose (data cutoff 25 Jun 2020) were conducted in pts with HR-MDS/AML who received sabatolimab (dosed at 240 or 400 mg Q2W or 800 mg Q4W) + HMA. Results: Sabatolimab PK was similar for pts with solid tumors (n=252), HR-MDS, and AML (n=155 HR-MDS + AML); no drug-drug interactions were seen for any combinations. The estimated half-life of sabatolimab was ~16.7 days at linear PK dose levels and moderate accumulation was seen after multiple dosing. At lower doses (≤80 mg Q2W or ≤240 mg Q4W), sabatolimab exhibited nonlinear elimination indicative of target-mediated drug disposition, potentially related to internalization of the membrane-bound antibody-TIM-3 complex. At doses ≥240 mg Q2W and ≥800 mg Q4W, a plateau in the accumulated total soluble TIM-3 level was reached and PK approached a proportional dose-exposure relationship. Based on popPK modeling, among sabatolimab + HMA regimens 400 mg Q2W had the highest Ctrough at steady state, and 800 mg was predicted to be an equivalent Q4W dosing regimen. Both doses had similar steady state Cavg and similarly high occupancy rates for membrane-bound TIM-3 in the BM (>95% in ≥95% of pts with HR-MDS/AML), suggesting similarly high levels of TIM-3 engagement. PK exposure-safety analysis included 102 pts with HR-MDS/AML who received sabatolimab + HMA and were categorized into 4 exposure quartiles based on steady state Cmax and Cavg. There was no relationship between steady state Cmax or Cavg quartiles and incidence of treatment-related AEs. Similarly, exposure-efficacy analysis (n=92) showed no clear relationship between steady state Ctrough or Cavg and percent BM blast reduction or clinical benefit (CR/mCR/CRi/PR). The effect of sabatolimab dose on safety/efficacy was also evaluated in an updated clinical analysis in pts with HR-MDS/AML treated with sabatolimab + HMA. Overall, sabatolimab + HMA was safe and well tolerated with a low rate of study discontinuation due to AE (3.4% [4/116]). Rates of most common gr ≥3 treatment-emergent AEs and rates of gr ≥3 possible immune-mediated AEs related to study treatment did not appear to be dose dependent (Table). Among 35 evaluable pts with HR-MDS, CR/mCR/PR rates were 50.0%, 33.3% and 54.5% at sabatolimab doses of 240 mg Q2W, 400 mg Q2W and 800 mg Q4W. Among 60 evaluable pts with AML, CR/CRi/PR rates were 35.3%, 37.5% and 31.6%, respectively. There were no notable differences in responses across the 3 doses (Table). Conclusion: Sabatolimab 400 mg Q2W was predicted to have the highest steady state Ctrough and TIM-3 occupancy rate when combined with HMA, and 800 mg was predicted to be an equivalent Q4W dosing regimen. No clear relationship was seen between sabatolimab dose or steady state exposure and safety/efficacy at the doses tested. These results support clinical development of the sabatolimab 400 mg Q2W and 800 mg Q4W dosing regimens. Co-senior authors Andrew Brunner and Uma Borate contributed equally to the work. Table Disclosures Wei: AbbVie: Honoraria, Research Funding, Speakers Bureau; Pfizer: Honoraria; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria; Amgen: Honoraria, Research Funding; Walter and Eliza Hall Institute of Medical Research: Patents & Royalties; Novartis: Honoraria, Research Funding, Speakers Bureau; Genetech: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Research Funding; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Consultancy, Honoraria, Research Funding. Porkka:BMS/Celgene: Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Knapper:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Garcia-Manero:Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Acceleron Pharmaceuticals: Consultancy, Honoraria; Onconova: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; H3 Biomedicine: Research Funding; AbbVie: Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; Novartis: Research Funding; Jazz Pharmaceuticals: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Amphivena Therapeutics: Research Funding. Wermke:MacroGenics: Honoraria. Janssen:MSD: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Roche: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Daiichi-Sankyo: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Takeda: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Janssen: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Abbvie: Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda; BMS: Other: Founder of the HematologyApp which is supported by Janssen, BMS, Incyte, MSD, Pfizer, Daiichi-Sankyo, Roche and Takeda, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Traer:Notable Labs: Consultancy, Current equity holder in private company; Genentech: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Astellas: Membership on an entity's Board of Directors or advisory committees. Narayan:Sanofi-Genzyme: Other: Current Spouse employment ; Takeda: Other: Prior Spouse employment within 24 months; Genentech: Other: Prior Spouse employment within 24 months and prior spouse equity divested within past 24 months. Kontro:Abbvie: Research Funding; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Ottmann:Amgen: Honoraria, Research Funding; Novartis: Honoraria; Celgene: Honoraria, Research Funding; Fusion Pharma: Honoraria; Incyte: Honoraria, Research Funding. Liao:Novartis: Current Employment. Stein:Novartis: Current Employment, Divested equity in a private or publicly-traded company in the past 24 months, Ended employment in the past 24 months. Khanshan:Novartis: Current Employment. Naidu:Novartis Pharmaceuticals: Current Employment. Zhang:Novartis: Current Employment. Rinne:Novartis: Current Employment; Qiagen: Consultancy. Sun:Novartis: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Brunner:Biogen: Consultancy; Acceleron Pharma Inc.: Consultancy; Celgene/BMS: Consultancy, Research Funding; Forty Seven, Inc: Consultancy; Jazz Pharma: Consultancy; Novartis: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Xcenda: Consultancy; GSK: Research Funding; Janssen: Research Funding; Astra Zeneca: Research Funding. Borate:Genentech: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Research Funding; AbbVie: Other: Investigator in AbbVie-funded clinical trials; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding.

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.

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.

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

Abstract 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.

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.