Intramedullary nailing of subtrochanteric fractures in elderly patients: Comparative study of helical blade cephalomedullary nail versus reconstruction nail.

INTRODUCTION: Intramedullary nailing (IMN), which is a common method for treating subtrochanteric fractures, is conducted as cephalomedullary (CMN) or reconstruction (RCN) nailing. Numerous studies have reported the effectiveness of CMN, which requires a shorter surgery time and provides stronger fixation strength with blade-type devices. However, the radiographic and clinical outcomes of the use of CMN and RCN in elderly patients aged ≥65 years have not been compared yet. This study aimed to investigate whether CMN offers superior outcomes over RCN in the treatment of subtrochanteric fractures in elderly patients. MATERIALS AND METHODS: This retrospective study included 60 elderly patients (17 men and 43 women; mean age: 74.9 years) diagnosed with subtrochanteric fractures and treated with IMN with helical blade CMN (CMN group: 30 patients) or RCN (RCN group: 30 patients) between January 2013 and December 2018 with at least 1 year of follow-up period. Radiologic outcomes were evaluated based on the postoperative state of alignment and the achievement and timing of bony union at the final follow-up. Clinical outcomes were evaluated using the Merle d'Aubigné-Postel score. Radiologic and clinical outcomes in the two groups were compared and analyzed, and the occurrence of complications was examined. RESULTS: The difference in malalignment between the two groups was not significant; however, the RCN group achieved more effective reduction. At the final follow-up, bony union was achieved within 18.9 weeks, on average, in 28 patients in the CMN group and within 21.6 weeks, on average, in 27 patients in the RCN group. Twenty patients in the CMN group and 26 in the RCN group showed good or better results according to the Merle d'Aubigné-Postel score. No significant differences were found for any of the parameters. CONCLUSIONS: In the treatment of difficult subtrochanteric fractures in elderly patients, RCN can provide excellent reduction and strong fixation similar to CMN and can result in outstanding clinical and radiologic outcomes.

Object Manipulation with an Anthropomorphic Robotic Hand via Deep Reinforcement Learning with a Synergy Space of Natural Hand Poses.

Anthropomorphic robotic hands are designed to attain dexterous movements and flexibility much like human hands. Achieving human-like object manipulation remains a challenge especially due to the control complexity of the anthropomorphic robotic hand with a high degree of freedom. In this work, we propose a deep reinforcement learning (DRL) to train a policy using a synergy space for generating natural grasping and relocation of variously shaped objects using an anthropomorphic robotic hand. A synergy space is created using a continuous normalizing flow network with point clouds of haptic areas, representing natural hand poses obtained from human grasping demonstrations. The DRL policy accesses the synergistic representation and derives natural hand poses through a deep regressor for object grasping and relocation tasks. Our proposed synergy-based DRL achieves an average success rate of 88.38% for the object manipulation tasks, while the standard DRL without synergy space only achieves 50.66%. Qualitative results show the proposed synergy-based DRL policy produces human-like finger placements over the surface of each object including apple, banana, flashlight, camera, lightbulb, and hammer.

Staged Management of Severely Displaced Calcaneal Fractures With Transarticular Pinning: A Damage Control Strategy.

BACKGROUND: Severely displaced calcaneal fractures can result in considerable morphology derangement and may be accompanied by soft tissue compromise. Delayed operative restoration of the calcaneal morphology may result in acute retensioning of the damaged soft tissue with associated wound-related complications. In this study, we describe a staged treatment of displaced intra-articular calcaneal fractures that uses temporary transarticular Kirschner wire (K-wire) fixation and staged conversion to definite fixation. METHODS: We identified all of the patients who were treated at our institution for calcaneal fractures between 2015 and 2019. A total of 17 patients with 20 calcaneal fractures were selectively treated with 2-stage management. Temporary transarticular K-wire fixation was performed 24 hours after the injury to restore calcaneal morphology and the surrounding soft tissue. After the soft tissue was considered safe, delayed open reduction and internal fixation was performed. The time to definite surgery, radiographic alignment, wound complications, time to radiographic union, and hindfoot American Orthopaedic Foot & Ankle Society (AOFAS) scores were recorded. RESULTS: The average follow-up period was 17 months (range, 12-43). The average Böhler angle increased from a mean of -22 degrees (range, -109 to 25) to 25 degrees (range, 0 to 47) after temporary transarticular K-wire fixation. The mean time from temporary pinning to conversion to definite internal fixation was 20 (range, 10-32) days. There were no immediate postoperative complications. The average time to radiographic union was 13.7 (range, 10-16) weeks. The mean AOFAS score was 87 (range, 55-100). No infections or wound complications were reported during the follow-up period. CONCLUSION: Temporary transarticular pinning for staged calcaneal fracture treatment is safe and effective in restoring the calcaneal morphology. This novel and relatively simple method may facilitate delayed operation and decrease wound-related complications. LEVEL OF EVIDENCE: Level IV, retrospective case series.

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms.

BACKGROUND AND OBJECTIVE: Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions. METHODS: In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5-fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast. RESULTS: The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbreast dataset. CONCLUSION: The capability of the YOLO detector boosted the classification models to achieve a promising breast lesion diagnostic performance. Such prediction results should help to develop a feasible CAD system for practical breast cancer diagnosis.

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.

BACKGROUND AND OBJECTIVE: Computer automated diagnosis of various skin lesions through medical dermoscopy images remains a challenging task. METHODS: In this work, we propose an integrated diagnostic framework that combines a skin lesion boundary segmentation stage and a multiple skin lesions classification stage. Firstly, we segment the skin lesion boundaries from the entire dermoscopy images using deep learning full resolution convolutional network (FrCN). Then, a convolutional neural network classifier (i.e., Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201) is applied on the segmented skin lesions for classification. The former stage is a critical prerequisite step for skin lesion diagnosis since it extracts prominent features of various types of skin lesions. A promising classifier is selected by testing well-established classification convolutional neural networks. The proposed integrated deep learning model has been evaluated using three independent datasets (i.e., International Skin Imaging Collaboration (ISIC) 2016, 2017, and 2018, which contain two, three, and seven types of skin lesions, respectively) with proper balancing, segmentation, and augmentation. RESULTS: In the integrated diagnostic system, segmented lesions improve the classification performance of Inception-ResNet-v2 by 2.72% and 4.71% in terms of the F1-score for benign and malignant cases of the ISIC 2016 test dataset, respectively. The classifiers of Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201 exhibit their capability with overall weighted prediction accuracies of 77.04%, 79.95%, 81.79%, and 81.27% for two classes of ISIC 2016, 81.29%, 81.57%, 81.34%, and 73.44% for three classes of ISIC 2017, and 88.05%, 89.28%, 87.74%, and 88.70% for seven classes of ISIC 2018, respectively, demonstrating the superior performance of ResNet-50. CONCLUSIONS: The proposed integrated diagnostic networks could be used to support and aid dermatologists for further improvement in skin cancer diagnosis.

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews's correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies. In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.

BACKGROUND AND OBJECTIVE: Automatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma. METHODS: In this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN). The proposed FrCN method directly learns the full resolution features of each individual pixel of the input data without the need for pre- or post-processing operations such as artifact removal, low contrast adjustment, or further enhancement of the segmented skin lesion boundaries. We evaluated the proposed method using two publicly available databases, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets. To evaluate the proposed method, we compared the segmentation performance with the latest deep learning segmentation approaches such as the fully convolutional network (FCN), U-Net, and SegNet. RESULTS: Our results showed that the proposed FrCN method segmented the skin lesions with an average Jaccard index of 77.11% and an overall segmentation accuracy of 94.03% for the ISBI 2017 test dataset and 84.79% and 95.08%, respectively, for the PH2 dataset. In comparison to FCN, U-Net, and SegNet, the proposed FrCN outperformed them by 4.94%, 15.47%, and 7.48% for the Jaccard index and 1.31%, 3.89%, and 2.27% for the segmentation accuracy, respectively. Furthermore, the proposed FrCN achieved a segmentation accuracy of 95.62% for some representative clinical benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases in the ISBI 2017 test dataset, exhibiting better performance than those of FCN, U-Net, and SegNet. CONCLUSIONS: We conclude that using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.

Treatment of infra-isthmal femoral fracture with an intramedullary nail: Is retrograde nailing a better option than antegrade nailing?

INTRODUCTION: Antegrade intramedullary (IM) nailing is ideal for femoral shaft fractures, but fixing the fracture distal to the isthmal level may be difficult because of medullary canal widening and the proximity of fracture location from the distal femoral joint line. This study aimed to compare treatment results between antegrade and retrograde nailing for infra-isthmal femoral shaft fracture, and to identify influencing factors of nonunion and malalignment. MATERIALS AND METHODS: Sixty patients with infra-isthmal femoral shaft fractures treated with IM nailing and followed-up for > 1 year were enrolled in this retrospective study, 38 in the antegrade nailing group, and 22 in the retrograde nailing group. The two groups had no significant differences in age, sex, and fracture location (p = 0.297, Mann-Whitney test). Radiological evaluation was performed, and functional result was assessed using the Knee Society scoring system. Complications were analyzed in accordance with fracture location, fracture type, and operative method. RESULTS: According to the AO/OTA classification, 35, 16, and 9 cases were type A (A1: 1, A2: 11, A3: 23), B (B1: 2, B2: 7, B3: 7), and C fractures (C2: 4, C3: 5), respectively. The mean follow-up duration was 29.5 months. In the antegrade and retrograde nailing groups, the primary bony union rates were 73.7% in 20.7 weeks (range 12-41) and 86.4% in 17.4 weeks (range 12-30), respectively. The two groups showed no significant differences in union rate (p = 0.251, Pearson's Chi-square test) and union time (p = 0.897, Mann-Whitney test). No cases of malalignment of > 10° in any plane were found in both groups. The mean Knee Society scores were 92 (range 84-100) and 91 (range 83-95) in the antegrade and retrograde nailing groups, respectively, showing no significant difference (p = 0.297, Pearson's Chi-square test). Although fracture location was not significantly related to union rate (p = 0.584, Mann-Whitney test), patients with an effective working length of the distal segment of < 0.75 were prone to nonunion (p = 0.003, Pearson's Chi-square test). CONCLUSIONS: Although no significant difference was found in IM nail type, the IM nail with a shorter working length distal to the fracture showed a strong relationship with nonunion.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

BACKGROUND AND OBJECTIVE: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. METHODS: The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. RESULTS: Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. CONCLUSIONS: Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions.

Medial Patellofemoral Ligament Reconstruction for Recurrent Patellar Instability Using a Gracilis Autograft without Bone Tunnel.

BACKGROUND: Several tendon graft and fixation methods have been introduced in medial patellofemoral ligament (MPFL) reconstruction for recurrent patellar dislocation. The purpose of this study was to evaluate the results of MPFL reconstruction using a gracilis autograft fixation without bone tunnel in patients with recurrent patellar instability. METHODS: Nine patients (four males and five females) diagnosed with recurrent patellar instability from July 2009 to January 2013 and had MPFL reconstruction using a gracilis autograft were included. The average age of the patients was 24.6 years (range, 13 to 48 years), and the average follow-up period was 19.3 months (range, 12 to 30 months). For every patient, femoral attachment was fixed using suture anchors securing the patella by suturing the periosteum and surrounding soft tissue. Clinical evaluation included the Kujala, Lysholm, and Tegner scores; in addition, patients were examined for any complication including recurrent dislocation. The congruence angle and patella alta were assessed radiologically before and after surgery. RESULTS: The Kujala score improved from an average of 42.7 ± 8.4 before surgery to 79.6 ± 13.6 (p = 0.008) at final follow-up; the Lysholm score improved from 45.8 ± 5.7 to 82.0 ± 10.5 (p = 0.008); and the Tegner score improved from 2.8 ± 0.8 to 5.6 ± 1.5 (p = 0.007). The Insall-Salvati ratio changed from 1.16 ± 0.1 (range, 0.94 to 1.35) before surgery to 1.14 ± 0.1 (range, 0.96 to 1.29; p = 0.233) at the final follow-up without significance. The congruence angle significantly improved from 26.5° ± 10.6° (range, 12° to 43°) before surgery to -4.0° ± 4.3° (range, -12° to 5°; p = 0.008) at final follow-up. Subluxation was observed in one patient and hemarthrosis occurred in another patient 2 years after surgery, but these patients were asymptomatic. CONCLUSIONS: We achieved good results with a patellar fixation technique in MPFL reconstruction using a gracilis autograft employing soft tissue suturing in patients with recurrent patellar dislocation.

Structure-based design of novel class II c-Met inhibitors: 2. SAR and kinase selectivity profiles of the pyrazolone series.

As part of our effort toward developing an effective therapeutic agent for c-Met-dependent tumors, a pyrazolone-based class II c-Met inhibitor, N-(4-((6,7-dimethoxyquinolin-4-yl)oxy)-3-fluorophenyl)-1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazole-4-carboxamide (1), was identified. Knowledge of the binding mode of this molecule in both c-Met and VEGFR-2 proteins led to a novel strategy for designing more selective analogues of 1. Along with detailed SAR information, we demonstrate that the low kinase selectivity associated with class II c-Met inhibitors can be improved significantly. This work resulted in the discovery of potent c-Met inhibitors with improved selectivity profiles over VEGFR-2 and IGF-1R that could serve as useful tools to probe the relationship between kinase selectivity and in vivo efficacy in tumor xenograft models. Compound 59e (AMG 458) was ultimately advanced into preclinical safety studies.

Structure-based design of novel class II c-Met inhibitors: 1. Identification of pyrazolone-based derivatives.

Deregulation of c-Met receptor tyrosine kinase activity leads to tumorigenesis and metastasis in animal models. More importantly, the identification of activating mutations in c-Met, as well as MET gene amplification in human cancers, points to c-Met as an important target for cancer therapy. We have previously described two classes of c-Met kinase inhibitors (class I and class II) that differ in their binding modes and selectivity profiles. The class II inhibitors tend to have activities on multiple kinases. Knowledge of the binding mode of these molecules in the c-Met protein led to the design and evaluation of several new class II c-Met inhibitors that utilize various 5-membered cyclic carboxamides to conformationally restrain key pharmacophoric groups within the molecule. These investigations resulted in the identification of a potent and novel class of pyrazolone c-Met inhibitors with good in vivo activity.

Discovery and optimization of a series of benzothiazole phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) dual inhibitors.

Phosphoinositide 3-kinase α (PI3Kα) is a lipid kinase that plays a key regulatory role in several cellular processes. The mutation or amplification of this kinase in humans has been implicated in the growth of multiple tumor types. Consequently, PI3Kα has become a target of intense research for drug discovery. Our studies began with the identification of benzothiazole compound 1 from a high throughput screen. Extensive SAR studies led to the discovery of sulfonamide 45 as an early lead, based on its in vitro cellular potency. Subsequent modifications of the central pyrimidine ring dramatically improved enzyme and cellular potency and led to the identification of chloropyridine 70. Further arylsulfonamide SAR studies optimized in vitro clearance and led to the identification of 82 as a potent dual inhibitor of PI3K and mTOR. This molecule exhibited potent enzyme and cell activity, low clearance, and high oral bioavailability. In addition, compound 82 demonstrated tumor growth inhibition in U-87 MG, A549, and HCT116 tumor xenograft models.

A study on the feasibility of active contours on automatic CT bone segmentation.

Automatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV AC's performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images.

Finite element simulation of ultrasound propagation in bone for quantitative ultrasound toward the diagnosis of osteoporosis.

Osteoporosis is a serious bone disease which leads to the increased risk of bone fractures. For prevention and therapy, early detection of osteoporosis is critical. In general, for diagnosis of osteoporosis, dual-energy X-ray absoptiometry (DXA) or densitometry is most commonly used. However DXA exhibits some disadvantages such as ionizing radiation, relatively expensive cost, and limited information on mineralization and geometry of the bone. As an alternative method of DXA, quantitative ultrasound (QUS) is being investigated. In contrast to DXA, QUS is non-ionizing and relatively inexpensive. It can also provide some bone-related parameters (e.g., quantitative measurements including speed of sound and frequency-dependent attenuation). However the estimation of these parameters is difficult and few analytical solutions exist due to the complex behavior of ultrasound propagation in bone. As an alternative to the analytical methods, in most attempts, finite difference time domain (FDTD) method is used for simulation of ultrasound propagation in bone with a limited capability of modeling complex geometries of the bone. Finite element method (FEM) is a better solution since it can handle the complex geometry, but has been rarely applied due to its computational complexity. In this work, we propose an approach of FEM-based simulation of ultrasound propagation in bone. To validate our approach, we have tested simulated and real bone models from micro-CT using the index of speed-of-sound. Our results achieve an average of 97.54% in the computational accuracy.

Design, synthesis, and biological evaluation of potent c-Met inhibitors.

c-Met is a receptor tyrosine kinase that plays a key role in several cellular processes but has also been found to be overexpressed and mutated in different human cancers. Consequently, targeting this enzyme has become an area of intense research in drug discovery. Our studies began with the design and synthesis of novel pyrimidone 7, which was found to be a potent c-Met inhibitor. Subsequent SAR studies identified 22 as a more potent analog, whereas an X-ray crystal structure of 7 bound to c-Met revealed an unexpected binding conformation. This latter finding led to the development of a new series that featured compounds that were more potent both in vitro and in vivo than 22 and also exhibited different binding conformations to c-Met. Novel c-Met inhibitors have been designed, developed, and found to be potent in vitro and in vivo.

Identification of a novel recepteur d'origine nantais/c-met small-molecule kinase inhibitor with antitumor activity in vivo.

Recepteur d'origine nantais (RON) is a receptor tyrosine kinase closely related to c-Met. Both receptors are involved in cell proliferation, migration, and invasion, and there is evidence that both are deregulated in cancer. Receptor overexpression has been most frequently described, but other mechanisms can lead to the oncogenic activation of RON and c-Met. They include activating mutations or gene amplification for c-Met and constitutively active splicing variants for RON. We identified a novel inhibitor of RON and c-Met, compound I, and characterized its in vitro and in vivo activities. Compound I selectively and potently inhibited the kinase activity of RON and c-Met with IC(50)s of 9 and 4 nmol/L, respectively. Compound I inhibited hepatocyte growth factor-mediated and macrophage-stimulating protein-mediated signaling and cell migration in a dose-dependent manner. Compound I was tested in vivo in xenograft models that either were dependent on c-Met or expressed a constitutively active form of RON (RONDelta160 in HT-29). Compound I caused complete tumor growth inhibition in NIH3T3 TPR-Met and U-87 MG xenografts but showed only partial inhibition in HT-29 xenografts. The effect of compound I in HT-29 xenografts is consistent with the expression of the activating b-Raf V600E mutation, which activates the mitogen-activated protein kinase pathway downstream of RON. Importantly, tumor growth inhibition correlated with the inhibition of c-Met-dependent and RON-dependent signaling in tumors. Taken together, our results suggest that a small-molecule dual inhibitor of RON/c-Met has the potential to inhibit tumor growth and could therefore be useful for the treatment of patients with cancers where RON and/or c-Met are activated.

Discovery of a potent, selective, and orally bioavailable c-Met inhibitor: 1-(2-hydroxy-2-methylpropyl)-N-(5-(7-methoxyquinolin-4-yloxy)pyridin-2-yl)-5-methyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazole-4-carboxamide (AMG 458).

Deregulation of the receptor tyrosine kinase c-Met has been implicated in human cancers. Pyrazolones with N-1 bearing a pendent hydroxyalkyl side chain showed selective inhibition of c-Met over VEGFR2. However, studies revealed the generation of active, nonselective metabolites. Blocking this metabolic hot spot led to the discovery of 17 (AMG 458). When dosed orally, 17 significantly inhibited tumor growth in the NIH3T3/TPR-Met and U-87 MG xenograft models with no adverse effect on body weight.

c-Met inhibitors with novel binding mode show activity against several hereditary papillary renal cell carcinoma-related mutations.

c-Met is a receptor tyrosine kinase often deregulated in human cancers, thus making it an attractive drug target. One mechanism by which c-Met deregulation leads to cancer is through gain-of-function mutations. Therefore, small molecules capable of targeting these mutations could offer therapeutic benefits for affected patients. SU11274 was recently described and reported to inhibit the activity of the wild-type and some mutant forms of c-Met, whereas other mutants are resistant to inhibition. We identified a novel series of c-Met small molecule inhibitors that are active against multiple mutants previously identified in hereditary papillary renal cell carcinoma patients. AM7 is active against wild-type c-Met as well as several mutants, inhibits c-Met-mediated signaling in MKN-45 and U-87 MG cells, and inhibits tumor growth in these two models grown as xenografts. The crystal structures of AM7 and SU11274 bound to unphosphorylated c-Met have been determined. The AM7 structure reveals a novel binding mode compared with other published c-Met inhibitors and SU11274. The molecule binds the kinase linker and then extends into a new hydrophobic binding site. This binding site is created by a significant movement of the C-helix and so represents an inactive conformation of the c-Met kinase. Thus, our results demonstrate that it is possible to identify and design inhibitors that will likely be active against mutants found in different cancers.