Roche (Sweden)

In-silico antibody lead optimization remains challenging due to scarce high-quality data, costly experimental validation, and the need to jointly optimize multiple developability properties. Discovery workflows often rely on high-throughput phage, ribosome or yeast display experiments, which yield large but noisy datasets; as leads emerge, strategies shift to low-throughput assays which are precise, yet unscalable. Deep-learning and language-model approaches are hindered by such limited, unreliable measurements. We introduce Property Enhancer ( PropEn ), a data-efficient framework for low-data, heterogeneous regimes that can simultaneously optimize multiple antibody properties. PropEn proposes a matching-based augmentation that expands the training data with sequence pairs differing by only a few mutations; within each pair the second sequence improves the target value, providing an implicit optimization signal. Extensive in silico and in vitro tests show 10–39× affinity gains across four targets and nine leads, and enable joint multi-property optimization, positioning PropEn as a scalable, general solution.

Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule cocrystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE neural network potential (NNP), trained on a large database of density functional theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams.

Abstract Lung-resident immune cells, spanning both innate and adaptive compartments, preserve the integrity of the respiratory barrier, but become pathogenic if dysregulated1. Current in vitro organoid models aim to replicate interactions between the alveolar epithelium and immune cells but have not yet incorporated lung-specific immune cells critical for tissue residency2. Here we address this shortcoming by describing human lung alveolar immuno-organoids (LIO) that contain an autologous tissue-resident lymphoid compartment, primarily composed of tissue-resident memory T cells (TRMs). Additionally, we introduce lung alveolar immuno-organoids with myeloid cells (LIOM), which include both TRMs and a macrophage-rich alveolar myeloid compartment. The resident immune cells formed a stable immune-epithelial system, frequently interacting with the epithelium and promoting a regenerative alveolar transcriptomic profile. To understand how dysregulated inflammation perturbed the respiratory barrier, we simulated T-cell-mediated inflammation in LIOs and LIOMs and used single-cell transcriptomic analyses to uncover the molecular mechanisms driving immune responses. The presence of innate cells induced a shift in T cell identity from cytotoxic to immunosuppressive, reducing epithelial cell killing and inflammation. Based on insights obtained with bulk RNA-seq data from the phase 3 IMpower150 trial, we tested whether LIOM cultures could model clinically-relevant but poorly understood pulmonary side effects caused by immunotherapies such as the checkpoint inhibitor atezolizumab3. We observed a decrease in immunosuppressive T cells and identified gene signatures that matched the transcriptomic profile of patients with drug-induced pneumonitis. Given its effectiveness in capturing outcomes and mechanisms associated with a prevalent pulmonary disease, this system unlocks opportunities for studying a wide range of immune-related pathologies in the lung.

Human neurons programmed through transcription factor (TF) overexpression model neuronal differentiation and neurological diseases. However, programming specific neuron types remains challenging. Here, we modulate developmental signaling pathways combined with TF overexpression to explore the spectrum of neuron subtypes generated from pluripotent stem cells. We screened 480 morphogen signaling modulations coupled with NGN2 or ASCL1/DLX2 induction using a multiplexed single-cell transcriptomic readout. Analysis of 700,000 cells identified diverse excitatory and inhibitory neurons patterned along the anterior-posterior and dorsal-ventral axes of neural tube development. We inferred signaling and TF interaction networks guiding differentiation of forebrain, midbrain, hindbrain, spinal cord, peripheral sympathetic and sensory neurons. Our approach provides a strategy for cell subtype programming and to investigate how cooperative signaling drives neuronal fate.

Introduction:Tumor-stroma ratio (TSR) is prognostic in multiple cancers, while its role in high-grade serous ovarian cancer (HGSOC) remains unclear. Despite the prognostic insight gained from genetic profiles and tumor-infiltrating lymphocytes (TILs), the prognostic use of histology slides remains limited, while it enables the identification of tumor characteristics via computational pathology reducing scoring time and costs. To address this, this study aimed to assess TSR’s prognostic role in HGSOC and its association with TILs. We additionally developed an algorithm, Ovarian-TSR (OTSR), using deep learning for TSR scoring, comparing it to manual scoring.Methods:340 patients with advanced-stage who underwent primary debulking surgery (PDS) or neo-adjuvant chemotherapy (NACT) with interval debulking (IDS). TSR was assessed in both the most invasive (MI) and whole tumor (WT) regions through manual scoring by pathologists and quantification using OTSR. Patients were categorized as stroma-rich (≥ 50% stroma) or stroma-poor (< 50%). TILs were evaluated via immunohistochemical staining.Results:In PDS, stroma-rich tumors were significantly associated with a more frequent papillary growth pattern (60% vs 34%), while In NACT stroma-rich tumors had a lower Tumor Regression Grading (TRG 4&5, 21% vs 57%) and increased pleural metastasis (25% vs 16%). Stroma-rich patients had significantly shorter overall and progression-free survival compared to stroma-poor (31 versus 45 months; P < 0.0001, and 15 versus 17 months; P = 0.0008, respectively). Combining stromal percentage and TILs led to three distinct survival groups with good (stroma-poor, high TIL), medium (stroma-rich, high TIL, or; stroma-poor, Low TIL), and poor(stroma-rich, low TIL) survival. These survival groups remained significant in CD8 and CD103 in multivariable analysis (Hazard ratio (HR) = 1.42, 95% Confidence-interval (CI) = 1.02–1.99; HR = 1.49, 95% CI = 1.01–2.18, and HR = 1.48, 95% CI = 1.05–2.08; HR = 2.24, 95% CI = 1.55–3.23, respectively). OTSR was able to recapitulate these results and demonstrated high concordance with expert pathologists (correlation = 0.83).Conclusions:TSR is an independent prognostic factor for survival assessment in HGSOC. Stroma-rich tumors have a worse prognosis and, in the case of NACT, a higher likelihood of pleural metastasis. OTSR provides a cost and time-efficient way of determining TSR with high reproducibility and reduced inter-observer variability.

Abstract Accelerated digitalization in the health sector requires the development of appropriate evaluation methods to ensure digital health technologies (DHTs) are safe and effective. Clinical simulation can be used by researchers to test DHTs in an agile and low-cost way, yet there is limited research on criteria to assess the robustness of simulations and subsequently, their relevance for a regulatory decision. The aim of this study was to gain consensus the research question “What criteria should be used to assess clinical simulation being used to generate evidence for software as a medical device (SaMD)?” 39 international experts in the digital health field, including academics, regulators, policy makers, and industry representatives, participated in a three-round eDelphi exercise. Options were generated through the scoping questionnaire around key themes identified from the literature to obtain a comprehensive list of criteria and voted upon in two subsequent questionnaire rounds. Consensus was defined by two criteria: if &lt;10% of the panelists deemed the criteria as ‘not important’ or ‘not important at all’ and &gt;60% ‘important’ or ‘very important’. 43 criteria gained consensus from the panelists across seven domains and the Simulation for Regulation of SaMD (SIROS) framework was developed. We highlight key areas of concern identified by panelists, specifically on the importance of fidelity of simulation and its reporting, and the challenge of bias. Future research should prioritise the development of safe and effective SaMD, while implementing the criteria generated for regulating DHT based on clinical simulation evidence can enable faster uptake of technologies.

Practice environments have a significant impact on nurses' practice and their retention within the oncology and hematology specialty. Understanding how specific elements of the practice environment impact nurse outcomes is important for creating supportive and safe practice environments. To evaluate the impact of the practice environment on oncology and hematology nurses. A scoping review was conducted according to the PRISMA-ScR Statement Guidelines. Electronic databases (MEDLINE, CINAHL, PsychINFO, Google Scholar, and Scopus) were searched using key terms. Articles were assessed according to the eligibility criteria. Data extraction was conducted with results explained through descriptive analysis. One thousand seventy-eight publications were screened with 32 publications meeting the inclusion criteria. The 6 elements of the practice environment (workload, leadership, collegial relations, participation, foundations, and resources) were found to significantly impact nurses' job satisfaction, psychological well-being, levels of burnout, and intention to leave. Negative practice environment elements were linked to increased levels of job dissatisfaction, higher levels of burnout, greater prevalence of psychological distress, and greater intention to leave both oncology and hematology nursing and the nursing profession. The practice environment has a significant impact on nurses, their job satisfaction, well-being, and intention to stay. This review will inform future research and forthcoming practice change to provide oncology and hematology nurses with practice environments that are safe and lead to positive nurse outcomes. This review provides a foundation upon which to develop and implement tailored interventions that best support oncology and hematology nurses to remain in practice and provide high-quality care.