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Real-World Effectiveness and Safety of SDZ-ADL (Adalimumab Biosimilar) in Patients with Psoriasis from the British Association of Dermatologists Biologic and Immunomodulators Register (BADBIR)

SDZ-ADL is a biosimilar of reference adalimumab. Here, the safety and effectiveness data of SDZ-ADL from the British Association of Dermatologists Biologic and Immuno-modulators Register (BADBIR) are reported. In the safety set, data of SDZ-ADL were compared with conventional systemics data. In the effectiveness set, the effectiveness and quality-of-life of patients treated with SDZ-ADL as a first-time biologic, or who switched from a previous biologic to SDZ-ADL, were assessed using the Psoriasis Activity Severity Index (PASI) and Dermatology Life Quality Index (DLQI), respectively. A total of 565 (incidence rate (IR) per 1000 person-years 29.1, 95% CI 26.8–31.6) serious infections and 48 (IR 2.5, 95% CI 1.8–3.3) myocardial infarction events were reported in the conventional systemics cohort compared with four (IR 31.5, 95% CI 8.6–80.7) and one (IR 7.9, 95% CI 0.2–43.9) in the biologic cohort, respectively. One patient (0.7% (1/136)) reported injection-site pain in the biologic cohort. At 12 months, PASI ≤ 2 was achieved in 84.6% (11/13) and 76.9% (10/13) and DLQI 0/1 was achieved in 70% (7/10) and 75% (3/4) of patients in the biologic-naïve and biologic-switch cohorts, respectively. After one year of therapy, 82.7% (110/133) patients remained on SDZ-ADL. SDZ-ADL was well-tolerated and effective in patients with psoriasis.

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Open Access
Comparing different metrics on an anisotropic depth completion model

This paper discussed an anisotropic interpolation model that filling in-depth data in a largely empty region of a depth map. We consider an image with an anisotropic metric gi⁢j that incorporates spatial and photometric data. We propose a numerical implementation of our model based on the “eikonal” operator, which compute the solution of a degenerated partial differential equation (the biased Infinity Laplacian or biased Absolutely Minimizing Lipschitz Extension). This equation’s solution creates exponential cones based on the available data, extending the available depth data and completing the depth map image. Because of this, this operator is better suited to interpolate smooth surfaces. To perform this task, we assume we have at our disposal a reference color image and a depth map. We carried out an experimental comparison of the AMLE and bAMLE using various metrics with square root, absolute value, and quadratic terms. In these experiments, considered color spaces were sRGB, XYZ, CIE-L*⁢a*⁢b*, and CMY. In this document, we also presented a proposal to extend the AMLE and bAMLE to the time domain. Finally, in the parameter estimation of the model, we compared EHO and PSO. The combination of sRGB and square root metric produces the best results, demonstrating that our bAMLE model outperforms the AMLE model and other contemporary models in the KITTI depth completion suite dataset. This type of model, such as AMLE and bAMLE, is simple to implement and represents a low-cost implementation option for similar applications.

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Hybrid Pipeline Infinity Laplacian Plus Convolutional Stage Applied to Depth Completion

Abstract Interpolation of the sparse depth map is a fundamental task for applications such as video games or autonomous vehicles. Frequently, depth maps present a lack of information due to sensor misinterpretations or occlusion. Traditional image processing methods have been used to tackle this interpolation problem. Nowadays, convolutional neural networks have been used to solve this problem with diverse approaches. On one hand, traditional methods are simple to implement, but the model is constructed by the designer. On the other hand, convolutional networks need a large database in order to train a model and are far to be implemented in a real platform such as FPGA. The question is if it is possible to state a hybrid model that considers the advantages of both points of view. Our proposal considers the infinity Laplacian (or AMLE) which is the most straightforward operator that satisfies a set of simple axioms. Embedding a reference color image with an anisotropic metric, we propose a three-stage pipeline to interpolate depth maps: color features selection, AMLE solution, and post-filtering. Inspired by convolutional networks, we constructed two convolution max pooling stages for color features selection. Taking those features, we used them as the color reference image, which defines the metric \(g_{ij}\), and then we solved the AMLE. Finally, a second convolutional stage was applied to the interpolated map. The obtained results show that the proposed convolutional stage can be replaced by a median filter. This fact increases the performance of the complete pipeline, and this pipeline outperforms contemporary methods in the KITTI dataset showing clearly the contribution of a convolutional preprocessing stage. The convolution stage enforces strong edges and smooth textures in the reference image to let the anisotropic diffusion process fill in the empty regions bounded by strong edges.KeywordsDepth completionAbsolutely minimizing Lipschitz extensionConvolutional stage

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Level A IVIVC for immediate release tablets confirms in vivo predictive dissolution testing for ibuprofen

A bioequivalence study comparing two fixed dose combination tablets containing 200 mg ibuprofen and 30 mg pseudoephedrine hydrochloride showed bioequivalence for pseudoephedrine AUC and Cmax, but the reference product showed higher Cmax than the test product in fasted conditions. The main difference between products was the presence of tribasic calcium phosphate in the reference tablet, resulting in an increased surface pH of the dissolving ibuprofen particles under gastric and intestinal conditions and, consequently, higher solubility of ibuprofen. A mechanistic model based on mass balance and ionization equilibria was used to calculate the pH of the particle surface under different buffer conditions. The discrepancies in surface pH between test and reference tablet were pronounced in 0.1 M and 0.01 M hydrochloric acid and in diluted maleate 7 mM pH 6.5 and phosphate 5 mM pH 6.7 buffers (but negligible in compendial phosphate buffer pH 6.8. Only those dissolution tests using pre-treatment in acidic conditions could be used to build a one-step in vitro-in vivo correlation (IVIVC). This work shows the potential of these discriminatory and in vivo predictive dissolution methods to obtain IVIVCs for BCS class IIa drugs and for extending BCS biowaivers to BCS class IIa drugs.

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Open Access
Ten years experience with the first approved biosimilar recombinant human growth hormone drug in normal clinical practice

Abstract Introduction Recombinant human growth hormone (rhGH) is the first biosimilar drug approved by the European Medicines Agency in 2006, using the biosimilar registration process. It was authorised for the treatment of growth hormone deficiency, and growth disorders associated with Turner's syndrome, chronic renal failure, Prader-Willi syndrome, and growth disorders in children/adolescents born small for gestational age, and replacement therapy in adults with pronounced growth hormone deficiency. Materials and methods This review is focused on the scientific evidence published about this drug in the last ten years, including the clinical trials on which the approval of the regulatory authority is based, and the most relevant studies evaluating the clinical impact of the drug in clinical practice. Results The equivalence between biosimilar and original product has been confirmed in the clinical trials published by Romer et al. and Lopez-Siguero et al. Furthermore, studies carried out in real-life conditions confirm its long-term efficacy and safety, as well as the absence of clinical impact by switching treatment from the original to the biosimilar product. Conclusion The number of patients receiving this medication has continuously increased since its approval. Its equivalence with the original product has been verified. Preliminary data from the post-authorisation PATRO study confirm the efficacy and safety of the biosimilar product in comparison with data from clinical trials. However, final results must be evaluated at the end of the study, which will provide additional information about the long-term efficacy and safety of the biosimilar drug.

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Open Access
Ten years experience with the first approved biosimilar recombinant human growth hormone drug in normal clinical practice

Recombinant human growth hormone (rhGH) is the first biosimilar drug approved by the European Medicines Agency in 2006, using the biosimilar registration process. It was authorised for the treatment of growth hormone deficiency, and growth disorders associated with Turner's syndrome, chronic renal failure, Prader-Willi syndrome, and growth disorders in children/adolescents born small for gestational age, and replacement therapy in adults with pronounced growth hormone deficiency. This review is focused on the scientific evidence published about this drug in the last ten years, including the clinical trials on which the approval of the regulatory authority is based, and the most relevant studies evaluating the clinical impact of the drug in clinical practice. The equivalence between biosimilar and original product has been confirmed in the clinical trials published by Romer et al. and López-Siguero et al. Furthermore, studies carried out in real-life conditions confirm its long-term efficacy and safety, as well as the absence of clinical impact by switching treatment from the original to the biosimilar product. The number of patients receiving this medication has continuously increased since its approval. Its equivalence with the original product has been verified. Preliminary data from the post-authorisation PATRO study confirm the efficacy and safety of the biosimilar product in comparison with data from clinical trials. However, final results must be evaluated at the end of the study, which will provide additional information about the long-term efficacy and safety of the biosimilar drug.

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Open Access
Epidemiology and characteristics of febrile neutropenia in oncology patients from Spanish tertiary care hospitals: PINNACLE study.

Febrile neutropenia (FN) is one of the most common adverse events associated with myelosuppressive chemotherapy for cancer treatment. The objective of this study was to describe the incidence of hospitalization due to FN in Spanish tertiary care hospitals (PINNACLE study). This epidemiological, retrospective, multicenter, nationwide study involved 119 patients from oncology units of 10 Spanish tertiary care hospitals who were admitted for FN. The primary endpoint was to assess the epidemiology and characteristics of FN. The incidence of admissions due to FN in oncology patients was 2.0% (interquartile range [IQR], 1.6-3.0). In terms of fever and absolute neutrophil count (ANC), 37.0% of the patients had a temperature of ≥38.2°C and an ANC of ≤500/m3. The number of patients who received prophylactic treatment with granulocyte colony-stimulating factor (G-CSF) was significantly higher in the palliative group (32.6%) compared with that in the non-palliative group (13.5%). The hospital length of stay was significantly shorter in patients who received prophylactic G-CSF compared with those who did not (5.0 days; IQR, 4.0-9.0 vs. 7.0 days; IQR, 5.0-11.0, respectively). The hospital length of stay was also significantly shorter in patients receiving palliative treatment (5.0 days; IQR, 3.0-7.0) compared with those receiving non-palliative therapy (7.0 days; IQR, 5.0-12.0). In conclusion, the incidence of admissions due to FN in oncology patients was 2.0% and the duration of hospital stay was 7.0 days. Prophylactic G-CSF treatment was found to be associated with better outcomes and shorter hospital stays. Therefore, the use of this treatment becomes relevant for achieving better clinical outcomes and reducing hospitalization cost in the management of FN.

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Open Access