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Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure–response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression‐free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting. [ABSTRACT FROM AUTHOR]

Accurate theoretical calculations of particle deposition in a 90° bend rely on understanding its flow field characteristics. However, given the complexity of the flow patterns within the bend, most mathematical models utilize relatively simplified simulated flow fields. This study collected experimental data on aerosol particle losses in 90° bends, facilitating a clearer model selection. In addition, it also aimed to experimentally investigate the impact of expanding the cross-section of a bend and subsequently generalize these findings as design criteria. The results revealed that none of the models could accurately predict the experimental data concerning particles ranging from 1.4 to 10 μm in diameter and with 32 channels per decade sizing resolution. While the Reynolds number (Re) is a conventional metric for flow characterization, our study underscores the superior relevance of the Dean number (De) in bends due to centripetal force and secondary flow effects. Hence, a novel empirical equation was proposed after analyzing experimental data encompassing a De range of 1000 to 4200 and a Stokes number (Stk) range of 0.001 to 10. This equation solely relies on Stk to calculate the transport efficiency of particles in bends. In contrast, the model proposed by Pui et al. demonstrated a tendency to overestimate the transport efficiency of particles with Stk > 0.2 while underestimating the transport efficiency of particles with Stk < 0.2. Furthermore, extending the cross-section of the 90° bend by 0.2 times the bend diameter at the 45° position was shown to improve particles' transport efficiency significantly. [ABSTRACT FROM AUTHOR]

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN's deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective. [ABSTRACT FROM AUTHOR]

A multistate platform model was developed to describe time‐to‐event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression‐free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well‐described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan‐Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time‐variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations. [ABSTRACT FROM AUTHOR]

In cancer treatment, the first-generation, cytotoxic drugs, though effective against cancer cells, also harmed healthy ones. The second-generation targeted cancer cells precisely to inhibit their growth. Enter the third-generation, consisting of immuno-oncology drugs, designed to combat drug resistance and bolster the immune system's defenses. These advanced therapies operate by obstructing the uncontrolled growth and spread of cancer cells through the body, ultimately eliminating them effectively. Within the arsenal of cancer treatment, monoclonal antibodies offer several advantages, including inducing cancer cell apoptosis, precise targeting, prolonged presence in the body, and minimal side effects. A recent development in cancer therapy is Antibody-Drug Conjugates (ADCs), initially developed in the mid-20th century. The second generation of ADCs addressed this issue through innovative antibody modification techniques, such as DAR regulation, amino acid substitutions, incorporation of non-natural amino acids, and enzymatic drug attachment. Currently, a third generation of ADCs is in development. This study presents an overview of 12 available ADCs, reviews 71 recent research papers, and analyzes 128 clinical trial reports. The overarching objective is to gain insights into the prevailing trends in ADC research and development, with a particular focus on emerging frontiers like potential targets, linkers, and drug payloads within the realm of cancer treatment. [ABSTRACT FROM AUTHOR]

Monoclonal antibodies (mAbs) have become important classes in biopharmaceutical products. The selection of antibodies has traditionally been based on their binding affinity. New techniques and advancements in combinatorial antibody libraries have made it possible to isolate functional antibodies within cellular environments. These antibodies can act an agonist or antagonist provide valuable insights into the complexities of signal transduction and potentially leading to different cell responses or affecting cell behavior. Additionally, an in vivo selection system is utilized, which relies on cell migration to specific tissues, to generate antibodies that induce cells to differentiate and selectively migrate to particular tissues. Overall, the discovery of functional antibodies from antibody libraries has the potential to reveal receptor pleiotropism and pave opportunities to investigate cellular biology and uncover surprising discoveries in the areas of stem cells and cancer research. [ABSTRACT FROM AUTHOR]