Purpose Pharmacokinetic (PK) differences between the extended half-life (EHL) factor IX (FIX) concentrates for hemophilia B exist, which may influence hemostatic efficacy of replacement therapy in patients. Therefore, we aimed to evaluate the PK properties of three EHL-FIX concentrates and compare them to a standard half-life (SHL) recombinant FIX (rFIX) concentrate. Methods Activity-time profiles of PEGylated FIX (N9-GP), FIX linked with human albumin (rIX-FP), FIX coupled to human IgG1 Fc-domain (rFIXFc), and SHL rFIX were simulated for 10,000 patients during steady-state dosing of 40 IU/kg once weekly (EHL-FIX) and biweekly (rFIX) using published concentrate specific population PK models. Results Half-lives were respectively 80, 104, and 82 h for N9-GP, rIX-FP, and rFIXFc versus 22 h for rFIX. Between the EHL concentrates, exposure was different with area under the curve (AUC) values of 78.5, 49.6, and 12.1 IU/h/mL and time above FIX target values of 0.10 IU/mL of 168, 168, and 36 h for N9-GP, rIX-FP, and rFIXFc, respectively. N9-GP produced the highest median in vivo recovery value (1.70 IU/dL per IU/kg) compared with 1.18, 1.00, and 1.05 IU/dL per IU/kg for rIX-FP, rFIXFc, and rFIX, respectively. Conclusions When comparing EHL products, not only half-life but also exposure must be considered. In addition, variation in extravascular distribution of the FIX concentrates must be taken into account. This study provides insight into the different PK properties of these concentrates and may aid in determination of dosing regimens of EHL-FIX concentrates in real-life.
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative solutions have been proposed through differential optimization. Current approaches tend to optimize classification and separation jointly: aligning inputs with class vectors and separating class vectors angularly. This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations. The main observation behind our approach is that separation does not require optimization but can be solved in closed-form prior to training and plugged into a network. We outline a recursive approach to obtain the matrix consisting of maximally separable vectors for any number of classes, which can be added with negligible engineering effort and computational overhead. Despite its simple nature, this one matrix multiplication provides real impact. We show that our proposal directly boosts classification, long-tailed recognition, out-of-distribution detection, and open-set recognition, from CIFAR to ImageNet. We find empirically that maximum separation works best as a fixed bias; making the matrix learnable adds nothing to the performance. The closed-form implementation and code to reproduce the experiments are on github.Preprint. Under review.
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