Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer.

Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)-AlexNet, VGG, MobileNet, GoogLeNet, and ResNet-using a leave-two patient-out cross validation approach, and we evaluated the networks' performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient's response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.

Application of parallel liquid chromatography/mass spectrometry for high throughput microsomal stability screening of compound libraries.

Solution-phase and solid-phase parallel synthesis and high throughput screening have enabled biologically active and selective compounds to be identified at an unprecedented rate. The challenge has been to convert these hits into viable development candidates. To accelerate the conversion of these hits into lead development candidates, early assessment of the physicochemical and pharmacological properties of these compounds is being made. In particular, in vitro absorption, distribution, metabolism, and elimination (ADME) assays are being conducted at earlier and earlier stages of discovery with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. In this report, we present an eight-channel parallel liquid chromatography/mass spectrometry (LC/MS) system in combination with custom Visual Basic and Applescript automated data processing applications for high throughput early ADME. The parallel LC/MS system was configured with one set of gradient LC pumps and an eight-channel multiple probe autosampler. The flow was split equivalently into eight streams before the multiple probe autosampler and recombined after the eight columns and just prior to the mass spectrometer ion source. The system was tested for column-to-column variation and for reproducibility over a 17 h period (approximately 500 injections per column). The variations in retention time and peak area were determined to be less than 2 and 10%, respectively, in both tests. The parallel LC/MS system described permits time-course microsomal incubations (t(o), t5, t15, t30) to be measured in triplicate and enables estimations of t 1/2 microsomal stability. The parallel LC/MS system is capable of analyzing up to 240 samples per hour and permits the complete profiling up to two microtiter plates of compounds per day (i.e., 176 test substrate compounds + sixteen controls).