12 research outputs found
Monitoring clearance of extractables and leachables from Single-Use Technologies by NMR
Process validation at Genentech requires NMR methods capable of detecting and reliably quantifying trace levels of process impurities and leachables. Measurements must be done in solutions which are far from ideal for NMR. Depending on the application, high protein and buffer concentrations, along with the presence of water, necessitate an approach to NMR measurements and data interpretation which differs greatly from traditional NMR methods. Here, we show how we deal with these complications, and how our methods may be applied to measuring the concentrations of leachables from single-use products
Headache be gone: Clearance of extractables and leachables in single-use technologies through ultrafiltration/diafiltration
Application of single-use technologies in biopharmaceutical manufacturing can be driven by several factors such as reduced capital costs, reduced risk of cross-contamination, increased process flexibility, and reduced cleaning validation. However, implementation of single-use technologies have been restricted due to a number of concerns, with the most commonly cited being the presence of extractables and leachables (E/L) from single-use technologies. In general, overly conservative estimates of E/L are used in the risk assessment due to lack of data on clearance, resulting in a time-consuming, costly, and extensive E/L assessment for single-use technologies.
A proof-of-concept study is presented here to simplify these E/L assessments for qualification and implementation of single-use technologies in biopharmaceutical manufacturing. Results from the study indicated clearance of defined E/L in protein solutions. However, unexpected clearance phenomena were observed for specific groups of E/L, which will be discussed in detail
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
The brightfield microscope is instrumental in the visual examination of both
biological and physical samples at sub-millimeter scales. One key clinical
application has been in cancer histopathology, where the microscopic assessment
of the tissue samples is used for the diagnosis and staging of cancer and thus
guides clinical therapy. However, the interpretation of these samples is
inherently subjective, resulting in significant diagnostic variability.
Moreover, in many regions of the world, access to pathologists is severely
limited due to lack of trained personnel. In this regard, Artificial
Intelligence (AI) based tools promise to improve the access and quality of
healthcare. However, despite significant advances in AI research, integration
of these tools into real-world cancer diagnosis workflows remains challenging
because of the costs of image digitization and difficulties in deploying AI
solutions. Here we propose a cost-effective solution to the integration of AI:
the Augmented Reality Microscope (ARM). The ARM overlays AI-based information
onto the current view of the sample through the optical pathway in real-time,
enabling seamless integration of AI into the regular microscopy workflow. We
demonstrate the utility of ARM in the detection of lymph node metastases in
breast cancer and the identification of prostate cancer with a latency that
supports real-time workflows. We anticipate that ARM will remove barriers
towards the use of AI in microscopic analysis and thus improve the accuracy and
efficiency of cancer diagnosis. This approach is applicable to other microscopy
tasks and AI algorithms in the life sciences and beyond
Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images
MET protein overexpression is a targetable event in non-small cell lung
cancer (NSCLC) and is the subject of active drug development. Challenges in
identifying patients for these therapies include lack of access to validated
testing, such as standardized immunohistochemistry (IHC) assessment, and
consumption of valuable tissue for a single gene/protein assay. Development of
pre-screening algorithms using routinely available digitized hematoxylin and
eosin (H&E)-stained slides to predict MET overexpression could promote testing
for those who will benefit most. While assessment of MET expression using IHC
is currently not routinely performed in NSCLC, next-generation sequencing is
common and in some cases includes RNA expression panel testing. In this work,
we leveraged a large database of matched H&E slides and RNA expression data to
train a weakly supervised model to predict MET RNA overexpression directly from
H&E images. This model was evaluated on an independent holdout test set of 300
over-expressed and 289 normal patients, demonstrating an ROC-AUC of 0.70 (95th
percentile interval: 0.66 - 0.74) with stable performance characteristics
across different patient clinical variables and robust to synthetic noise on
the test set. These results suggest that H&E-based predictive models could be
useful to prioritize patients for confirmatory testing of MET protein or MET
gene expression status
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images
Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for
immune checkpoint inhibitor therapy. However, MSI status is not routinely
tested in prostate cancer, in part due to low prevalence and assay cost. As
such, prediction of MSI status from hematoxylin and eosin (H&E) stained
whole-slide images (WSIs) could identify prostate cancer patients most likely
to benefit from confirmatory testing and becoming eligible for immunotherapy.
Prostate biopsies and surgical resections from de-identified records of
consecutive prostate cancer patients referred to our institution were analyzed.
Their MSI status was determined by next generation sequencing. Patients before
a cutoff date were split into an algorithm development set (n=4015, MSI-H 1.8%)
and a paired validation set (n=173, MSI-H 19.7%) that consisted of two serial
sections from each sample, one stained and scanned internally and the other at
an external site. Patients after the cutoff date formed the temporal validation
set (n=1350, MSI-H 2.3%). Attention-based multiple instance learning models
were trained to predict MSI-H from H&E WSIs. The MSI-H predictor achieved area
under the receiver operating characteristic curve values of 0.78 (95% CI
[0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the
internally prepared, externally prepared, and temporal validation sets,
respectively. While MSI-H status is significantly correlated with Gleason
score, the model remained predictive within each Gleason score subgroup. In
summary, we developed and validated an AI-based MSI-H diagnostic model on a
large real-world cohort of routine H&E slides, which effectively generalized to
externally stained and scanned samples and a temporally independent validation
cohort. This algorithm has the potential to direct prostate cancer patients
toward immunotherapy and to identify MSI-H cases secondary to Lynch syndrome
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge
Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.
Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe
Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge
Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.publishedVersionPeer reviewe