RESUMEN
Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.
RESUMEN
Pancreatic cancer claims over 460,000 victims per year. The carbohydrate antigen (CA) 19-9 test is the blood test used for pancreatic cancer's detection; however, its levels can be raised in symptomatic patients with other non-malignant diseases, or with other tumors in the surrounding area. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has demonstrated exceptional potential in cancer diagnostics, and its clinical implementation could represent a significant step towards early detection. This proof-of-concept study, investigating the use of ATR-FTIR spectroscopy on dried blood serum, focused on the discrimination of both cancer versus healthy control samples, and cancer versus symptomatic non-malignant control samples, as a novel liquid biopsy approach for pancreatic cancer diagnosis. Machine learning algorithms were applied, achieving results of up to 92% sensitivity and 88% specificity when discriminating between cancers (n = 100) and healthy controls (n = 100). An area under the curve (AUC) of 0.95 was obtained through receiver operating characteristic (ROC) analysis. Balanced sensitivity and specificity over 75%, with an AUC of 0.83, were achieved with cancers (n = 35) versus symptomatic controls (n = 35). Herein, we present these results as demonstration that our liquid biopsy approach could become a simple, minimally invasive, and reliable diagnostic test for pancreatic cancer detection.
RESUMEN
Background: Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reduce the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Methods: Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Results: Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields a sensitivity of 47% with 90% specificity (PPV 28.4%). Conclusions: This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate the identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.
RESUMEN
Early diagnosis of brain tumours is challenging and a major unmet need. Patients with brain tumours most often present with non-specific symptoms more commonly associated with less serious diagnoses, making it difficult to determine which patients to prioritize for brain imaging. Delays in diagnosis affect timely access to treatment, with potential impacts on quality of life and survival. A test to help identify which patients with non-specific symptoms are most likely to have a brain tumour at an earlier stage would dramatically impact on patients by prioritizing demand on diagnostic imaging facilities. This clinical feasibility study of brain tumour early diagnosis was aimed at determining the accuracy of our novel spectroscopic liquid biopsy test for the triage of patients with non-specific symptoms that might be indicative of a brain tumour, for brain imaging. Patients with a suspected brain tumour based on assessment of their symptoms in primary care can be referred for open access CT scanning. Blood samples were prospectively obtained from 385 of such patients, or patients with a new brain tumour diagnosis. Samples were analysed using our spectroscopic liquid biopsy test to predict presence of disease, blinded to the brain imaging findings. The results were compared to the patient's index brain imaging delivered as per standard care. Our test predicted the presence of glioblastoma, the most common and aggressive brain tumour, with 91% sensitivity, and all brain tumours with 81% sensitivity, and 80% specificity. Negative predictive value was 95% and positive predictive value 45%. The reported levels of diagnostic accuracy presented here have the potential to improve current symptom-based referral guidelines, and streamline assessment and diagnosis of symptomatic patients with a suspected brain tumour.
