Key metrics to expanding the pipeline of successful antibody–drug conjugates

Nessler, Ian; Menezes, Bruna; Thurber, Greg M.

doi:10.1016/j.tips.2021.07.005

Cited by 23 publications

(11 citation statements)

References 51 publications

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“…Poor tissue penetration of ADCs is a major limitation to their efficacy and has hampered wider clinical success of ADCs 11 , 26 , 34 , 36 , 37 . Higher antibody doses, whether via lower DAR 37 , 39 , 40 , more tolerable payloads 41 , or adding carrier doses 11 , 26 , 27 , 42 can improve the efficacy while lowering target-mediated uptake in healthy tissue to improve the therapeutic window. However, the lower (effective) DAR can also reduce efficacy in lower expression models 11 , 43 , 44 .…”

Section: Discussionmentioning

confidence: 99%

Design of high avidity and low affinity antibodies for in situ control of antibody drug conjugate targeting

Evans

Thurber

2022

Sci Rep

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Antibody-Drug Conjugates (ADCs) have rapidly expanded in the clinic, with 7 new approvals in 3 years. For solid tumors, high doses of ADCs improve tissue penetration and efficacy. These doses are enabled by lower drug-to-antibody ratios and/or co-administration of unconjugated antibody carrier doses to avoid payload toxicity. While effective for highly expressed targets, these strategies may not maintain efficacy with lower target expression. To address this issue, a carrier dose that adjusts binding in situ according to cellular expression was designed using computational modeling. Previous studies demonstrated that coadministration of unconjugated antibody with the corresponding ADC at an 8:1 ratio improves ADCs efficacy in high HER2 expressing tumors. By designing a High Avidity, Low Affinity (HALA) carrier antibody, ADC binding is partially blocked in high expression cells, improving tissue penetration. In contrast, the HALA antibody cannot compete with the ADC in low expressing cells, allowing ADC binding to the majority of receptors. Thus, the amount of competition from the carrier dose automatically adjusts to expression levels, allowing tailored competition between different patients/metastases. The computational model highlights two dimensionless numbers, the Thiele modulus and a newly defined competition number, to design an optimal HALA antibody carrier dose for any target.

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Section: Discussionmentioning

confidence: 99%

Design of high avidity and low affinity antibodies for in situ control of antibody drug conjugate targeting

Evans

Thurber

2022

Sci Rep

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“…As the biologics used to treat cancer increase in complexity, it is important to develop computational methods alongside animal experiments to better predict clinical outcomes. The fact that animal experiments can give opposite results depending on their design (e.g., a high or low DAR is more effective depending on the dose used) means that the preclinical outcomes are not necessarily providing fundamental insight into how the drug will behave in the clinic but rather how the drug behaves in that specific experiment ( Nessler et al, 2021 ). Computational predictions, grounded in experimental data, can help translate how these results will manifest in patients for better decision-making during development.…”

Section: Discussionmentioning

confidence: 99%

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

et al. 2022

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Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

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“…Trastuzumab deruxtecan comprises the topoisomerase inhibitor deruxtecan, a payload capable of bystander killing. This recently approved ADC has been reported to show increased in vivo efficacy in NCI-N87 xenografts compared to ado-trastuzumab emtansine, which uses a DM1 payload of similar in vitro potency. , Trastuzumab deruxtecan is approved to treat gastric cancer that has heterogeneous expression of HER2 and was recently reported to significantly improved both progression-free and overall survival in DESTINY-Breast04 trial in patients with HER2-low metastatic breast cancer. , …”

Section: Payload Propertiesmentioning

confidence: 99%

Structure–Activity Relationships of Antibody-Drug Conjugates: A Systematic Review of Chemistry on the Trastuzumab Scaffold

et al. 2022

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Antibody-drug conjugates (ADCs) are a rapidly growing class of cancer therapeutics that seek to overcome the low therapeutic index of conventional cytotoxic agents. However, realizing this goal has been a significant challenge. ADCs comprise several independently modifiable components, including the antibody, payload, linker, and bioconjugation method. Many approaches have been developed to improve the physical properties, potency, and selectivity of ADCs. The anti-HER-2 antibody trastuzumab, first approved in 1998, has emerged as an exceptional targeting agent for ADCs, as well as a broadly used platform for testing new technologies. The extensive work in this area enables the comparison of various linker strategies, payloads, drug-to-antibody ratios (DAR), and mode of attachment. In this review, these conjugates, ranging from the first clinically approved trastuzumab ADC, ado-trastuzumab emtansine (Kadcyla), to the latest variants are described with the goal of providing a broad overview, as well as enabling the comparison of existing and emerging conjugate technologies.

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Key metrics to expanding the pipeline of successful antibody–drug conjugates

Cited by 23 publications

References 51 publications

Design of high avidity and low affinity antibodies for in situ control of antibody drug conjugate targeting

Design of high avidity and low affinity antibodies for in situ control of antibody drug conjugate targeting

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Structure–Activity Relationships of Antibody-Drug Conjugates: A Systematic Review of Chemistry on the Trastuzumab Scaffold

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