Michalski Huettermann and Partner

In 2015, the author of this article has published a first review on the patent landscape of antibody drug conjugate s (ADCs).[1](#ref-0001) Now, 10 years later, it is time for a wrap-up. While the basic principles discussed in that earlier article sti

Background/objective: The aim of this study was to examine differences in bar velocity between the cambered and standard barbell bench press exercise.Methods: Ten healthy men volunteered for the study (age = 27.9 ± 3.7 years; body mass = 89.6 ± 11.7 kg; experience in resistance training 5.7 ± 2.1 years; bench press one-repetition maximum > 120% body mass).The first session aiming at the determination of the one-repetition maximum was followed by two experimental sessions consisted of performing 3 sets of 3 repetitions of the bench press exercise with the cambered or standard barbell at 50% of one-repetition maximum (of the standard barbell) in randomized order.Results: The two-way repeated measures ANOVA indicated a significant main effect of bar type on mean velocity (p=0.001;η 2 =0.739) and peak velocity (p=0.002;η 2 =0.661).The post-hoc analysis showed a significantly higher mean velocity for the cambered barbell compared to the standard barbell bench press in Set 1 (p=0.002)and Set 2 (p=0.012),but not in Set 3 (p=0.062).Moreover, there was a significantly higher mean velocity in Set 2, than in Set 1 (p=0.017)during the standard barbell bench press, with no other differences.Furthermore, a significantly higher peak velocity for the cambered barbell in comparison to the standard barbell was observed in all sets of the BP exercise (p<0.001;p=0.014; p=0.048; respectively).Conclusions: The outcomes of this investigation indicated that the cambered barbell used during the bench press training session significantly increases bar velocity compared to the standard barbell with the same external load across the workout.

Background: Diffusion-weighted imaging (DWI) is the gold standard for detection of acute ischemic stroke (AIS) and may guide patient selection for endovascular therapy. Machine learning algorithms can enhance clinical workflow in suspected AIS by providing early diagnosis and quantitative imaging biomarkers. We tested the hypothesis that our deep learning algorithm for detection of acute ischemia performs robustly in a real-world environment. Methods: We developed a novel deep neural network architecture (YNet) that operates on MRI diffusion data at full resolution. YNet demonstrated strong performance in AIS detection and segmentation on test and validation datasets. After validation, we challenged YNet to assess 1081 consecutive brain MRIs acquired from 9 different scanners at our institution over a 2-week period. Studies were labeled as having AIS by a radiologist who used the final clinical imaging report. A series selection algorithm was applied to each study to identify DWI and apparent diffusion coefficient (ADC) series for input to the YNet using DICOM metadata to ensure relevant criteria are met (B value = 1000, axial plane, etc.). Patients with known diagnoses were excluded. Results: The series selection algorithm identified 826 studies that had DWI and ADC series with the established criteria. After exclusion of patients with known diagnoses, 378 studies were assessed by the YNet algorithm for the presence of AIS. The prevalence of AIS was 11.9% within this consecutive cohort. The algorithm detected 43 out of the 45 studies with AIS. Sensitivity and specificity for detection were 95.5% and 97.0%, respectively. Conclusion: Our YNet algorithm demonstrated excellent performance in detecting AIS in a serially-acquired dataset. Fully automated implementation will require further refinement of the series selection algorithm.

Patents compensate an innovator for the efforts and resources invested into research and development. Because of the high development costs in the pharmaceutical industry, patents play a much greater role than in other industries. Based on their historical appearance in the market, active pharmaceutical ingredients can be grouped into at least four different classes, namely, small molecules, peptides, large biopharmaceutics, and nucleic acids. The term "patentability" means that an invention meets the substantial criteria set forth in the patent laws, including, in general, novelty and inventive step/non-obviousness. An appropriate patent lifecycle strategy reflects the different stages of a drug development process, plus new embodiments that represent second- or later-generation advancements of the said drug. In member states of EU, compound protection for an approved drug can be extended under Article 63(2) of the European Patent Convention (EPC) and Regulation (EC) 469/2009 by means of a Supplementary Protection Certificate (SPC).

Because drug development is not a static process, a drug’s market authorisation may change over time. In many cases, the number of indications for which a drug is approved increases. Because this facet of drug development also comes at significant costs, a corresponding patent filing strategy is required to protect these investments. The strategy as applied to rituximab, which is approved for a variety of indications, is discussed in this review.

It may be possible to patent both antibodies to new targets and later-developed antibodies to known targets.

In the last decade, therapeutic antibodies have become one of the commercially most successful classes of biopharmaceutical drugs. Major drug manufacturers who have successfully managed to occupy this new market, as well as biotechnology firms, some of which have experienced a quick growth and are now on par with the former, owe part of their success to suitable intellectual property strategies. This article provides an overview of the current thinking on antibody-related patents, and discusses strategies for protecting the antibody products of the future.

Schoppmann, Michael J. Esq.Editor(s): Capitulo, Kathleen Leask DNS, RN, FAAN Author Information