Cases

The approved step-up dosing (SUD) regimen for Tarlatamab, a T-cell engager targeting DLL3 in small cell lung cancer (SCLC), may lead to cytokine release syndrome (CRS) including grade 3 events. Alternative regimens were not explored in clinical trials. We applied mechanistic modeling to explore alternative SUD regimes for tarlatamab.

•  The model accurately described CRS incidence data for doses up to 30 mg, demonstrating adequate precision in both calibration and validation. CRS incidence for regimen 1/100 mg was overpredicted.
•  Alternative step-up dosing (SUD) regimens, such as 0.1 or 0.3 mg on day 1 followed by 1 mg on day 8, have demonstrated a lower incidence of CRS compared to the approved regimen.
• CD3 downmodulation via trimer internalization is a proposed mechanism for reduced CRS with SUD regimens.

To predict CD47 receptor occupancy (RO) by ALX148 in the tumor of lymphoma patients and to select RP2D.

Developed model successfully described clinical PK & CD47 RO ALX148 data. Model predicted that 30 mg/kg QoW and 10 mg/kg QW are the best regimen among tested in phase I trial on the basis of predict RO in the tumor tissues of lymphoma patients such as bone marrow (BM) and lymph nodes (LN).

to develop a minimal PBPK/RO model of mAb disposition that will be capable to predict pharmacokinetics and target receptor occupancy in plasma and in the site of action without fitting of clinical data on example of anti-PD1 mAbs.

• Developed model described clinical data on pharmacokinetics (PK) of anti-PD1 mAbs and PD-1 receptor occupancy in blood and tumor without fitting.
• Anti-PD-1 mAbs with Kd less than 3 nM will demonstrate trough receptor occupancy in tumor within the range 90-100% at doses starting from 3 mg/kg. So, we assume further increase in affinity of existing mAbs does not result.
• This PBPK/RO model can be used to predict RO in periphery and site of action for other mAbs and to select FIH dose and RP2D.

To apply QSP model AD:

1. To simulate combinations of emerging immunotherapies with treatments targeting cellular processes.
2. To formulate and check hypotheses about cognitive deterioration correlating with biomarkers presented in integrated model.

• The developed model describes correctly multiple preclinical data for antibodies treatment as well as treatments targeting neuronal metabolism, specifically mTor inhibition
• Among the cognition deterioration correlates chosen based on data caspase activity and tau describe aducanumab data satisfactorily, amyloid hypothesis contradicts to the data
• Inhibition of mTor (e.g. rapamycin) is predicted to boost efficacy of immunotherapy significantly and to be effective by itself. Model can be used to choose optimal schedule to provide safety of combination

Chronic obstructive pulmonary disease (COPD) is a debilitating desease characterized by progressive airflow limitation caused by cigarette smoking (CS). We aimed to develop a comprehensive model of cellular and molecular processes underlying CS-driven COPD progression (COPD platform) including the effects of standard medications (long-acting ?2-agonists (LABA) and inhaled corticosteroids (ICS)) on target reactions with a view to analyze disease mechanisms and evaluate the impact of existing therapies on COPD-specific biomarkers and clinical outcomes.

The COPD platform is capable of describing key aspects of COPD progression, including gradual decline in lung function. It allows definition of virtual patients according to their age, smoking history and baseline FEV1. The platform predicts the effect of standard treatments on clinical readouts and aids in understanding the drug efficacy. The COPD platform is currently being applied for evaluation of novel COPD therapies.

The main objectives of the presented work are:

• To develop quantitative systems pharmacology (QSP) model of systemic lupus erythematosus (SLE):
  • including the major immune response and inflammatory pathways in the context of SLE
  • taking into account potential therapeutic effects of a monoclonal antibody against type 1 interferon (IFN1) receptor (anti-IFNAR1)
  • designed for simulations of different virtual patients according to different SLE characteristics: baseline serum IFN1 and expression level of 21 IFN1-regulated genes (IFN gene signature, IFNGS), serum IFN1 and IFNGS during anti-IFNAR1 treatment, etc.
• To propose and explore possible mechanisms underlying observed variability in serum IFN1 changes (increase/decrease) after anti-IFNAR1 treatment.

The QSP model of SLE can be used to test therapeutic hypotheses and for identifying patient subsets in this highly heterogeneous disease.