Discovery of potent Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors with a type II inhibitor pharmacophore.

Malaria is a devastating disease that causes significant global morbidity and mortality. The rise of drug resistance against artemisinin-based combination therapy demonstrates the necessity to develop alternative antimalarials with novel mechanisms of action. We report the discovery of Ki8751 as an inhibitor of essential kinase PfPK6. 79 derivatives were designed, synthesized and evaluated for PfPK6 inhibition and antiplasmodial activity. Using group efficiency analyses, we established the importance of key groups on the scaffold consistent with a type II inhibitor pharmacophore. We highlight modifications on the tail group that contribute to antiplasmodial activity, cumulating in the discovery of compound 67, a PfPK6 inhibitor (IC50 = 13 nM) active against the P. falciparum blood stage (EC50 = 160 nM), and compound 79, a PfPK6 inhibitor (IC50 < 5 nM) with dual-stage antiplasmodial activity against P. falciparum blood stage (EC50 = 39 nM) and against P. berghei liver stage (EC50 = 220 nM).

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

Identification of Novel 2,4,5-Trisubstituted Pyrimidines as Potent Dual Inhibitors of Plasmodial PfGSK3/PfPK6 with Activity against Blood Stage Parasites In Vitro.

Essential plasmodial kinases PfGSK3 and PfPK6 are considered novel drug targets to combat rising resistance to traditional antimalarial therapy. Herein, we report the discovery of IKK16 as a dual PfGSK3/PfPK6 inhibitor active against blood stage Pf3D7 parasites. To establish structure-activity relationships for PfPK6 and PfGSK3, 52 analogues were synthesized and assessed for the inhibition of PfGSK3 and PfPK6, with potent inhibitors further assessed for activity against blood and liver stage parasites. This culminated in the discovery of dual PfGSK3/PfPK6 inhibitors 23d (PfGSK3/PfPK6 IC50 = 172/11 nM) and 23e (PfGSK3/PfPK6 IC50 = 97/8 nM) with antiplasmodial activity (23d Pf3D7 EC50 = 552 ± 37 nM and 23e Pf3D7 EC50 = 1400 ± 13 nM). However, both compounds exhibited significant promiscuity when tested in a panel of human kinase targets. Our results demonstrate that dual PfPK6/PfGSK3 inhibitors with antiplasmodial activity can be identified and can set the stage for further optimization efforts.

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

BACKGROUND: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples. MATERIALS AND METHODS: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone. RESULTS: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P << 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67-0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63-0.77, P ≤ 0.03). CONCLUSIONS: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.