Modern, highly sensitive assays often detect high rates of anti-drug antibodies (ADAs). However, the critical question for drug developers isn't the ADA incidence rate itself, but whether that immune response actually impacts drug exposure, efficacy, or overall patient outcome.
Related Insights
Non-human primate models are poor predictors of human immunogenicity. The industry has shifted to human-relevant ex vivo assays using whole blood or PBMCs. These tests can assess risks like complement activation upfront, enabling proactive protein engineering to improve a drug's safety profile.
To overcome on-target, off-tumor toxicity, LabGenius designs antibodies that act like biological computers. These molecules "sample" the density of target receptors on a cell's surface and are engineered to activate and kill only when a specific threshold is met, distinguishing high-expression cancer cells from low-expression healthy cells.
When debating immunotherapy risks, clinicians separate manageable side effects from truly life-altering events. Hypothyroidism requiring a daily pill is deemed acceptable, whereas toxicities like diabetes or myocarditis (each ~1% risk) are viewed as major concerns that heavily weigh on the risk-benefit scale for early-stage disease.
A critical distinction exists between a clinical adverse event (AE) and its impact on a patient's quality of life (QOL). For example, a drop in platelet count is a reportable AE, but the patient may be asymptomatic and feel fine. This highlights the need to look beyond toxicity tables to understand the true patient experience.
The current boom in immunology and autoimmune (I&I) therapeutics is not a separate phenomenon but a direct consequence of capital and knowledge from immuno-oncology. Many of the same biological pathways are being targeted, simply modulated down (for autoimmune) instead of up (for cancer), allowing for rapid therapeutic advancement and platform reuse.
While avoiding severe toxicities of older IL-2 drugs, Synthakyne's therapy causes a manageable rash. The company views this as a favorable, on-target effect, indicating the drug is successfully activating the immune system as intended, rather than as a problematic side effect.
The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.
The differing efficacy and toxicity profiles of TROP2 ADCs like sacituzumab govitecan and Dato-DXD suggest that the drug's linker and payload metabolism are crucial determinants of clinical outcome. This indicates that focusing solely on the target antigen is an oversimplification of ADC design and performance.
Experts believe the stark difference in complete response rates (5% vs 30%) between two major ADC trials is likely due to "noise"—variations in patient populations (e.g., more upper tract disease) and stricter central review criteria, rather than a fundamental difference in the therapies' effectiveness.
Bi-specific T-cell engagers (BiTEs) are highly immunogenic because the mechanism activating T-cells to kill cancer also primes them to mount an immune response against the drug itself. This 'collateral effect' is an inherent design challenge for this drug class.