Cell And Gene Therapy: Research, Development & Regulatory Guidance

 Use RO data to predict the minimum effective dose and the biologically optimal dose for a PD-1–targeting monoclonal antibody in humans. 🔬 Step 1: Known Parameters Parameter Value Target PD-1 (on activated T cells) Affinity (KD) 3 nM (i.e., 3 × 10⁻⁹ M) MAb MW 150 kDa Desired RO for efficacy ≥70% Plasma volume 3 L (approximate) Bioavailability (IV) 100% RO vs. plasma concentration Modeled by: RO = C / (C + KD) 🧮 Step 2: Determine Required Plasma Concentration for Desired RO Use the RO equation: R O = [ D r u g ] [ D r u g ] + K D RO = \frac{[Drug]}{[Drug] + K_D} RO = [ Dr ug ] + K D ​ [ Dr ug ] ​ For 70% RO: 0.7 = C C + 3 × 10 − 9 ⇒ C = 7 × 10 − 9   M 0.7 = \frac{C}{C + 3 \times 10^{-9}} \Rightarrow C = 7 \times 10^{-9}\ M 0.7 = C + 3 × 1 0 − 9 C ​ ⇒ C = 7 × 1 0 − 9   M Convert to µg/mL: C = 7 × 10 − 9   m o l / L × 150 , 000   g / m o l = 1.05   μ g / m L C = 7 \times 10^{-9}\ mol/L \times 150,000\ g/mol = 1.05\ \mu g/mL C = 7 × 1 0 − 9  ...

Receptor Occupancy (RO) plays a pivotal role in dose prediction for monoclonal antibodies (mAbs) by providing a quantitative bridge between drug exposure (pharmacokinetics, PK) and biological effect (pharmacodynamics, PD). When well-understood, RO helps identify doses that are both safe and effective , especially for biologics targeting immune or signaling receptors. 🎯 Key Roles of RO in Dose Prediction 1. Identifies the Minimum Effective Dose (MABEL) Low-level RO (e.g., 10–20%) can induce a measurable biological effect . This is critical for first-in-human (FIH) studies where safety is paramount. Dose is chosen to achieve minimal RO , reducing risk of systemic toxicity or cytokine storm. ✅ Application: For immune checkpoint inhibitors (like PD-1 mAbs), even partial RO can restore T-cell function. 2. Defines Biologically Effective Dose (BED) The BED is the dose that results in optimal receptor occupancy (e.g., 70–90%) linked to: Saturation of target in r...