Approach 1 |
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Same as Approach 1 in;5 Serum ConcMeas < LLoQ replaced by LLoQ/2 in both articles. |
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Requires the availability of clinical lot IC50
In general, prediction performance not as high as Approaches 3 and 4
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Approach 2 |
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Previous work in Ref5: Serum ConcMeas < LLoQ replaced by LLoQ/2 in the popPK model. This current work: Likelihood method used to account for serum ConcMeas < LLoQ in the popPK model. |
Less resource-intensive: does not require ID50 measured on any serum sample
Generally, as good as or better performance than Approach 1
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Requires the availability of clinical lot IC50
Compared to Approach 1, Approaches 2–4 require serum concentrations measured at multiple time-points to permit popPK modeling
In general, prediction performance not as high as Approaches 3 and 4
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Approach 3 |
popPK modeling of measured longitudinal serum concentrations from all individuals;
PD modeling of measured longitudinal serum concentrations and ID50 titers from individuals who have both types of data available;
Serum ID50Pred obtained by plugging in popPK model-predicted serum concentration from step 1 into the PD model of step 2.
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Previous work in Ref5: Serum ConcMeas < LLoQ replaced by LLoQ/2 in the popPK model; ID50Meas < LLoQ replaced by LLoQ/2 in the PD model. This current work: Likelihood method used to account for serum ConcMeas <LLoQ, and ID50Meas <LLoQ in the popPK and PD models. |
Compared to Approaches 1 & 2, does not require availability of the clinical lot IC50
Generally, better prediction performance than Approaches 1 and 2
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Approach 4 |
popPK modeling of measured longitudinal serum concentrations from all individuals; calculate serum IIP for all individuals;
PD modeling of calculated longitudinal serum IIP and ID50 titers from individuals who have both types of data available;
Serum ID50Pred obtained by plugging in calculated IIP from step 1 into the PD model of step 2.
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NA (newly described here) |
Accounts for not only serum concentrations but also the slope of the dose–response neutralization curve of the clinical lot product
Generally, better prediction performance than Approaches 1 and 2
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More resource-intensive: in addition to serum concentrations measured at multiple time-points to permit popPK modeling, requires ID50 measured on a subset of those PK serum samples to permit PD modeling
Compared to Approach 3, additionally requires the availability of both IC50 and IC80 to permit the calculation of IIP as the predictor of ID50 titers
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