Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.

PURPOSE: We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. METHODS: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. RESULTS: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. CONCLUSIONS: The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.

Radiological imaging protection: a study on imaging dose used while planning computed tomography for external radiotherapy in Japan.

Previous studies have primarily focused on quality of imaging in radiotherapy planning computed tomography (RTCT), with few investigations on imaging doses. To our knowledge, this is the first study aimed to investigate the imaging dose in RTCT to determine baseline data for establishing national diagnostic reference levels (DRLs) in Japanese institutions. A survey questionnaire was sent to domestic RT institutions between 10 October and 16 December 2021. The questionnaire items were volume computed tomography dose index (CTDIvol), dose-length product (DLP), and acquisition parameters, including use of auto exposure image control (AEC) or image-improving reconstruction option (IIRO) for brain stereotactic irradiation (brain STI), head and neck (HN) intensity-modulated radiotherapy (IMRT), lung stereotactic body radiotherapy (lung SBRT), breast-conserving radiotherapy (breast RT), and prostate IMRT protocols. Details on the use of motion-management techniques for lung SBRT were collected. Consequently, we collected 328 responses. The 75th percentiles of CTDIvol were 92, 33, 86, 23, and 32 mGy and those of DLP were 2805, 1301, 2416, 930, and 1158 mGy·cm for brain STI, HN IMRT, lung SBRT, breast RT, and prostate IMRT, respectively. CTDIvol and DLP values in institutions that used AEC or IIRO were lower than those without use for almost all sites. The 75th percentiles of DLP in each treatment technique for lung SBRT were 2541, 2034, 2336, and 2730 mGy·cm for free breathing, breath holding, gating technique, and real-time tumor tracking technique, respectively. Our data will help in establishing DRLs for RTCT protocols, thus reducing imaging doses in Japan.

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG). METHODS: A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of σ = 0.5 $\sigma = 0.5$ and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison. RESULTS: The detection and classification of TF and DLG error were feasible for σ = 0.5 $\sigma = 0.5$ ; however, a considerable reduction of accuracy was observed for σ = 1.0 $\sigma = 1.0$ depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for σ = 0.5 $\sigma = 0.5$ and 1.0. The gamma analysis could not detect errors. CONCLUSIONS: We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

Mathematical model combined with microdosimetric kinetic model for tumor volume calculation in stereotactic body radiation therapy.

We proposed a new mathematical model that combines an ordinary differential equation (ODE) and microdosimetric kinetic model (MKM) to predict the tumor-cell lethal effect of Stereotactic body radiation therapy (SBRT) applied to non-small cell lung cancer (NSCLC). The tumor growth volume was calculated by the ODE in the multi-component mathematical model (MCM) for the cell lines NSCLC A549 and NCI-H460 (H460). The prescription doses 48 Gy/4 fr and 54 Gy/3 fr were used in the SBRT, and the effect of the SBRT on tumor cells was evaluated by the MKM. We also evaluated the effects of (1) linear quadratic model (LQM) and the MKM, (2) varying the ratio of active and quiescent tumors for the total tumor volume, and (3) the length of the dose-delivery time per fractionated dose (tinter) on the initial tumor volume. We used the ratio of the tumor volume at 1 day after the end of irradiation to the tumor volume before irradiation to define the radiation effectiveness value (REV). The combination of MKM and MCM significantly reduced REV at 48 Gy/4 fr compared to the combination of LQM and MCM. The ratio of active tumors and the prolonging of tinter affected the decrease in the REV for A549 and H460 cells. We evaluated the tumor volume considering a large fractionated dose and the dose-delivery time by combining the MKM with a mathematical model of tumor growth using an ODE in lung SBRT for NSCLC A549 and H460 cells.

Multicomponent mathematical model for tumor volume calculation with setup error using single-isocenter stereotactic radiotherapy for multiple brain metastases.

We evaluated the tumor residual volumes considering six degrees-of-freedom (6DoF) patient setup errors in stereotactic radiotherapy (SRT) with multicomponent mathematical model using single-isocenter irradiation for brain metastases. Simulated spherical gross tumor volumes (GTVs) with 1.0 (GTV 1), 2.0 (GTV 2), and 3.0 (GTV 3)-cm diameters were used. The distance between the GTV center and isocenter (d) was set at 0-10 cm. The GTV was simultaneously translated within 0-1.0 mm (T) and rotated within 0°-1.0° (R) in the three axis directions using affine transformation. We optimized the tumor growth model parameters using measurements of non-small cell lung cancer cell lines' (A549 and NCI-H460) growth. We calculated the GTV residual volume at the irradiation's end using the physical dose to the GTV when the GTV size, d, and 6DoF setup error varied. The d-values that satisfy tolerance values (10%, 35%, and 50%) of the GTV residual volume rate based on the pre-irradiation GTV volume were determined. The larger the tolerance value set for both cell lines, the longer the distance to satisfy the tolerance value. In GTV residual volume evaluations based on the multicomponent mathematical model on SRT with single-isocenter irradiation, the smaller the GTV size and the larger the distance and 6DoF setup error, the shorter the distance that satisfies the tolerance value might need to be.

The impact of target positioning error and tumor size on radiobiological parameters in robotic stereotactic radiosurgery for metastatic brain tumors.

This study aimed to evaluate the effect of target positioning error (TPE) on radiobiological parameters, such as tumor control probability (TCP) and normal tissue complication probability (NTCP), in stereotactic radiosurgery (SRS) for metastatic brain tumors of different sizes using CyberKnife. The reference SRS plans were created using the circular cone of the CyberKnife for each spherical gross tumor volume (GTV) with diameters (φ) of 5, 7.5, 10, 15, and 20 mm, contoured on computed tomography images of the head phantom. Subsequently, plans involving TPE were created by shifting the beam center by 0.1-2.0 mm in three dimensions relative to the reference plans using the same beam arrangements. Conformity index (CI), generalized equivalent uniform dose (gEUD)-based TCP, and NTCP of estimated brain necrosis were evaluated for each plan. When the gEUD parameter "a" was set to - 10, the CI and TCP for the reference plan at the φ5-mm GTV were 0.90 and 80.8%, respectively. The corresponding values for plans involving TPE of 0.5-mm, 1.0-mm, and 2.0-mm were 0.62 and 77.4%, 0.40 and 62.9%, and 0.12 and 7.2%, respectively. In contrast, the NTCP for all GTVs were the same. The TCP for the plans involving a TPE of 2-mm was 7.2% and 68.8% at the φ5-mm and φ20-mm GTV, respectively. The TPEs corresponding to a TCP reduction rate of 3% at the φ5-mm and φ20-mm GTV were 0.41 and 0.99 mm, respectively. TPE had a significant effect on TCP in SRS for metastatic brain tumors using CyberKnife, particularly for small GTVs.

Radiobiological evaluation considering the treatment time with stereotactic radiosurgery for brain metastases.

Objective: We evaluated the radiobiological effect of the irradiation time with the interruption time of stereotactic radiosurgery (SRS) using CyberKnife® (CK) systemfor brain metastases. Methods: We used the DICOM data and irradiation log file of the 10 patients with brain metastases from non-small-cell lung cancer (NSCLC) who underwent brain SRS. We defined the treatment time as the sum of the dose-delivery time and the interruption time during irradiations, and we used a microdosimetric kinetic model (MKM) to evaluate the radiobiological effects of the treatment time. The biological parameters, i.e. α0, ß0, and the DNA repair constant rate (a + c), were acquired from NCI-H460 cell for the MKM. We calculated the radiobiological dose for the gross tumor volume (GTVbio) to evaluate the treatment time's effect compared with no treatment time as a reference. The D95 (%) and the Radiation Therapy Oncology Group conformity index (RCI) and Paddick conformity index (PCI) were calculated as dosimetric indices. We used several DNA repair constant rates (a + c) (0.46, 1.0, and 2.0) to assess the radiobiological effect by varying the DNA repair date (a + c) values. Results: The mean values of D95 (%), RCI, and PCI for GTVbio were 98.8%, 0.90, and 0.80, respectively, and decreased with increasing treatment time. The mean values of D95 (%), RCI, and PCI of GTVbio at 2.0 (a+c) value were 94.9%, 0.71, and 0.49, respectively. Conclusion: The radiobiological effect of the treatment time on tumors was accurately evaluated with brain SRS using CK. Advances in knowledge: There has been no published investigation of the radiobiological impact of the longer treatment time with multiple interruptions of SRS using a CK on the target dose distribution in a comparison with the use of a linac. Radiobiological dose assessment that takes into account treatment time in the physical dose in this study may allow more accurate dose assessment in SRS for metastatic brain tumors using CK.

Calculated relative biological effectiveness (RBE) for initial DNA double-strand breaks (DSB) from flattening filter and flattening filter-free 6 MV X-ray fields.

OBJECTIVES: We evaluated the radiobiological effectiveness based on the yields of DNA double-strand breaks (DSBs) of field induction with flattening filter (FF) and FF-free (FFF) photon beams. METHODS: We used the particle and heavy ion transport system (PHITS) and a water equivalent phantom (30 × 30 × 30 cm3) to calculate the physical qualities of the dose-mean lineal energy (yD) with 6 MV FF and FFF. The relative biological effectiveness based on the yields of DNA-DSBs (RBEDSB) was calculated for standard radiation such as 220 kVp X-rays by using the estimating yields of SSBs and DSBs. The measurement points used to calculate the in-field yD and RBEDSB were located at a depth of 3, 5, and 10 cm in the water equivalent phantom on the central axis. Measurement points at 6, 8, and 10 cm in the lateral direction of each of the three depths from the central axis were set to calculate the out-of-field yD and RBEDSB. RESULTS: The RBEDSB of FFF in-field was 1.7% higher than FF at each measurement depth. The RBEDSB of FFF out-of-field was 1.9 to 6.4% higher than FF at each depth measurement point. As the distance to out-of-field increased, the RBEDSB of FFF rose higher than those of FF. FFF has a larger RBEDSB than FF based on the yields of DNA-DSBs as the distance to out-of-field increased. CONCLUSIONS: The out-of-field radiobiological effect of FFF could thus be greater than that of FF since the spreading of the radiation dose out-of-field with FFF could be a concern compared to the FF. ADVANCES IN KNOWLEDGE: The RBEDSB of FFF of out-of-field might be larger than FF.

Radiobiological evaluation considering setup error on single-isocenter irradiation in stereotactic radiosurgery.

PURPOSE: We calculated the dosimetric indices and estimated the tumor control probability (TCP) considering six degree-of-freedom (6DoF) patient setup errors in stereotactic radiosurgery (SRS) using a single-isocenter technique. METHODS: We used simulated spherical gross tumor volumes (GTVs) with diameters of 1.0 cm (GTV 1), 2.0 cm (GTV 2), and 3.0 cm (GTV 3), and the distance (d) between the target center and isocenter was set to 0, 5, and 10 cm. We created the dose distribution by convolving the blur component to uniform dose distribution. The prescription dose was 20 Gy and the dose distribution was adjusted so that D95 (%) of each GTV was covered by 100% of the prescribed dose. The GTV was simultaneously rotated within 0°-1.0° (δR) around the x-, y-, and z-axes and then translated within 0-1.0 mm (δT) in the x-, y-, and z-axis directions. D95, conformity index (CI), and conformation number (CN) were evaluated by varying the distance from the isocenter. The TCP was estimated by translating the calculated dose distribution into a biological response. In addition, we derived the x-y-z coordinates with the smallest TCP reduction rate that minimize the sum of squares of the residuals as the optimal isocenter coordinates using the relationship between 6DoF setup error, distance from isocenter, and GTV size. RESULTS: D95, CI, and CN were decreased with increasing isocenter distance, decreasing GTV size, and increasing setup error. TCP of GTVs without 6DoF setup error was estimated to be 77.0%. TCP were 25.8% (GTV 1), 35.0% (GTV 2), and 53.0% (GTV 3) with (d, δT, δR) = (10 cm, 1.0 mm, 1.0°). The TCP was 52.3% (GTV 1), 54.9% (GTV 2), and 66.1% (GTV 3) with (d, δT, δR) = (10 cm, 1.0 mm, 1.0°) at the optimal isocenter position. CONCLUSION: The TCP in SRS for multiple brain metastases with a single-isocenter technique may decrease with increasing isocenter distance and decreasing GTV size when the 6DoF setup errors are exceeded (1.0 mm, 1.0°). Additionally, it might be possible to better maintain TCP for GTVs with 6DoF setup errors by using the optimal isocenter position.

Conformational Plasticity of Cyclic Ras-Inhibitor Peptides Defines Cell Permeabilization Activity.

Cyclorasins 9A5 and 9A54 are 11-mer cyclic peptides that inhibit the Ras-Raf protein interaction. The peptides share a cell-penetrating peptide (CPP)-like motif; however, only cyclorasin 9A5 can permeabilize cells to exhibit strong cell-based activity. To unveil the structural origin underlying their distinct cellular permeabilization activities, we compared the three-dimensional structures of cyclorasins 9A5 and 9A54 in water and in the less polar solvent dimethyl sulfoxide (DMSO) by solution NMR. We found that cyclorasin 9A5 changes its extended conformation in water to a compact amphipathic structure with converged aromatic residues surrounded by Arg residues in DMSO, which might contribute to its cell permeabilization activity. However, cyclorasin 9A54 cannot adopt this amphipathic structure, due to the steric hindrance between two neighboring bulky amino-acid sidechains, Tle-2 and dVal-3. We also found that the bulkiness of the sidechains at positions 2 and 3 negatively affects the cell permeabilization activities, indicating that the conformational plasticity that allows the peptides to form the amphipathic structure is important for their cell permeabilization activities.

NMR assignments of the N-glycans of the Fc fragment of mouse immunoglobulin G2b glycoprotein.

The Fc portion of immunoglobulin G (IgG) promotes defensive effector functions in the immune system by interacting with Fcγ receptors and complement component C1q. These interactions critically depend on N-glycosylation at Asn297 of each CH2 domain, where biantennary complex-type oligosaccharides contain microheterogeneities resulting primarily from the presence or absence of non-reducing terminal galactose residues. Crystal structures of Fc have shown that a pair of N-glycans is located between the two CH2 domains. Here we applied our metabolic isotope labeling technique using mammalian cells for in-solution structural characterization of mouse IgG2b-Fc glycoforms with a molecular mass of 54 kDa. Based on spectral assignments of the N-glycans as well as polypeptide backbones of Fc, we probed conformational perturbations of Fc induced by N-glycan trimming, especially enzymatic degalactosylation. The results indicated that degalactosylation structurally perturbed the Fc region through rearrangement of glycan-protein interactions. The spectral assignments of IgG2b-Fc glycoprotein will provide the basis for NMR investigation of its dynamic conformations and interactions with effector molecules in solution.

Maximum distance in single-isocenter technique of stereotactic radiosurgery with rotational error using margin-based analysis.

Through geometrical simulation, we evaluated the effect of rotational error in patient setup on geometrical coverage and calculated the maximum distance between the isocenter and target, where the clinical PTV margin secures geometrical coverage with a single-isocenter technique. We used simulated spherical GTVs with diameters of 1.0 (GTV 1), 1.5 (GTV 2), 2.0 (GTV 3), and 3.0 cm (GTV 4). The location of the target center was set such that the distance between the target and isocenter ranged from 0 to 15 cm. We created geometrical coverage vectors so that each target was entirely covered by 100% of the prescribed dose. The vectors of the target positions were simultaneously rotated within a range of 0°-2.0° around the x-, y-, and z-axes. For each rotational error, the reduction in geometrical coverage of the targets was calculated and compared with that obtained for a rotational error of 0°. The tolerance value of the geometrical coverage reduction was defined as 5% of the GTV. The maximum distance that satisfied the 5% tolerance value for different values of rotational error at a clinical PTV margin of 0.1 cm was calculated. When the rotational errors were 0.5° for a 0.1 cm PTV margin, the maximum distances were as follows: GTV 1: 7.6 cm; GTV 2: 10.9 cm; GTV 3: 14.3 cm; and GTV 4: 21.4 cm. It might be advisable to exclude targets that are > 7.6 cm away from the isocenter with a single-isocenter technique to satisfy the tolerance value for all GTVs.

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). METHODS: Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error. RESULTS: The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. CONCLUSIONS: Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.

Radiobiological effects of the interruption time with Monte Carlo Simulation on multiple fields in photon beams.

PURPOSE: The interruption time is the irradiation interruption that occurs at sites and operations such as the gantry, collimator, couch rotation, and patient setup within the field in radiotherapy. However, the radiobiological effect of prolonging the treatment time by the interruption time for tumor cells is little evaluated. We investigated the effect of the interruption time on the radiobiological effectiveness with photon beams based on a modified microdosimetric kinetic (mMK) model. METHODS: The dose-mean lineal energy yD (keV/µm) of 6-MV photon beams was calculated by the particle and heavy ion transport system (PHITS). We set the absorbed dose to 2 or 8 Gy, and the interruption time (τ) was set to 1, 3, 5, 10, 30, and 60 min. The biological parameters such as α0, ß0, and DNA repair constant rate (a + c) values were acquired from a human non-small-cell lung cancer cell line (NCI-H460) for the mMK model. We used two-field and four-field irradiation with a constant dose rate (3 Gy/min); the photon beams were paused for interruption time τ. We calculated the relative biological effectiveness (RBE) to evaluate the interruption time's effect compared with no interrupted as a reference. RESULTS: The yD of 6-MV photon beams was 2.32 (keV/µm), and there was little effect by changing the water depth (standard deviation was 0.01). The RBE with four-field irradiation for 8 Gy was decreased to 0.997, 0.975, 0.900, and 0.836 τ = 1, 10, 30, 60 min, respectively. In addition, the RBE was affected by the repair constant rate (a + c) value, the greater the decrease in RBE with the longer the interruption time when the (a + c) value was large. CONCLUSION: The ~10-min interruption of 6-MV photon beams did not significantly impact the radiobiological effectiveness, since the RBE decrease was <3%. Nevertheless, the RBE's effect on tumor cells was decreased about 30% by increasing the 60 min interruption time at 8 Gy with four-field irradiation. It is thus necessary to make the interruption time as short as possible.

Effect of setup error in the single-isocenter technique on stereotactic radiosurgery for multiple brain metastases.

In conventional stereotactic radiosurgery (SRS), treatment of multiple brain metastases using multiple isocenters is time-consuming resulting in long dose delivery times for patients. A single-isocenter technique has been developed which enables the simultaneous irradiation of multiple targets at one isocenter. This technique requires accurate positioning of the patient to ensure optimal dose coverage. We evaluated the effect of six degrees of freedom (6DoF) setup errors in patient setups on SRS dose distributions for multiple brain metastases using a single-isocenter technique. We used simulated spherical gross tumor volumes (GTVs) with diameters ranging from 1.0 to 3.0 cm. The distance from the isocenter to the target's center was varied from 0 to 15 cm. We created dose distributions so that each target was entirely covered by 100% of the prescribed dose. The target's position vectors were rotated from 0°-2.0° and translated from 0-1.0 mm with respect to the three axes in space. The reduction in dose coverage for the targets for each setup error was calculated and compared with zero setup error. The calculated margins for the GTV necessary to satisfy the tolerance values for loss of GTV coverage of 3% to 10% were defined as coverage-based margins. In addition, the maximum isocenter to target distance for different 6DoF setup errors was calculated to satisfy the tolerance values. The dose coverage reduction and coverage-based margins increased as the target diameter decreased, and the distance and 6DoF setup error increased. An increase in setup error when a single-isocenter technique is used may increase the risk of missing the tumor; this risk increases with increasing distance from the isocenter and decreasing tumor size.

Targeting the cryptic sites: NMR-based strategy to improve protein druggability by controlling the conformational equilibrium.

Cryptic ligand binding sites, which are not evident in the unligated structures, are beneficial in tackling with difficult but attractive drug targets, such as protein-protein interactions (PPIs). However, cryptic sites have thus far not been rationally pursued in the early stages of drug development. Here, we demonstrated by nuclear magnetic resonance that the cryptic site in Bcl-xL exists in a conformational equilibrium between the open and closed conformations under the unligated condition. While the fraction of the open conformation in the unligated wild-type Bcl-xL is estimated to be low, F143W mutation that is distal from the ligand binding site can substantially elevate the population. The F143W mutant showed a higher hit rate in a phage-display peptide screening, and the hit peptide bound to the cryptic site of the wild-type Bcl-xL. Therefore, by controlling the conformational equilibrium in the cryptic site, the opportunity to identify a PPI inhibitor could be improved.

Dosimetric comparison of analytic anisotropic algorithm and Acuros XB algorithm in VMAT plans for high-grade glioma.

PURPOSE: To investigate the dosimetric impact between the anisotropic analytical algorithm (AAA) and the Acuros XB (AXB) algorithm in volumetric-modulated arc therapy (VMAT) plans for high-grade glioma (HGG). METHODS: We used a heterogeneous phantom to quantify the agreement between the measured and calculated doses from the AAA and from the AXB. We then analyzed 14 patients with HGG treated by VMAT, using the AAA. We newly created AXB plans for each corresponding AAA plan under the following conditions: (1) re-calculation for the same number of monitor units with an identical beam and leaf setup, and (2) re-optimization under the same conditions of dose constraints. The dose coverage for the planning target volume (PTV) was evaluated by dividing the coverage into the skull, air, and soft-tissue regions. RESULTS: Compared to the results obtained with the AAA, the AXB results were in good agreement with the measured profiles. The dose differences in the PTV between the AAA and re-calculated AXB plans were large in the skull region contained in the target. The dose difference in the PTV in both types of plan was significantly correlated with the volume of the skull contained in the target (r = 0.71, p = 0.0042). A re-optimized AXB plan's dose difference was lower vs. the re-calculated AXB plan's. CONCLUSIONS: We observed dose differences between the AAA and AXB plans, in particular in the cases in which the skull region of the target was large. Considering the phantom measurement results, the AXB algorithm should be used in VMAT plans for HGG.

Novel orally bioavailable EZH1/2 dual inhibitors with greater antitumor efficacy than an EZH2 selective inhibitor.

Polycomb repressive complex 2 (PRC2) methylates histone H3 lysine 27 and represses gene expression to regulate cell proliferation and differentiation. Enhancer of zeste homolog 2 (EZH2) or its close homolog EZH1 functions as a catalytic subunit of PRC2, so there are two PRC2 complexes containing either EZH2 or EZH1. Tumorigenic functions of EZH2 and its synthetic lethality with some subunits of SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complexes have been observed. However, little is known about the function of EZH1 in tumorigenesis. Herein, we developed novel, orally bioavailable EZH1/2 dual inhibitors that strongly and selectively inhibited methyltransferase activity of both EZH2 and EZH1. EZH1/2 dual inhibitors suppressed trimethylation of histone H3 lysine 27 in cells more than EZH2 selective inhibitors. They also showed greater antitumor efficacy than EZH2 selective inhibitor in vitro and in vivo against diffuse large B-cell lymphoma cells harboring gain-of-function mutation in EZH2. A hematological cancer panel assay indicated that EZH1/2 dual inhibitor has efficacy against some lymphomas, multiple myeloma, and leukemia with fusion genes such as MLL-AF9, MLL-AF4, and AML1-ETO. A solid cancer panel assay demonstrated that some cancer cell lines are sensitive to EZH1/2 dual inhibitor in vitro and in vivo. No clear correlation was detected between sensitivity to EZH1/2 dual inhibitor and SWI/SNF mutations, with a few exceptions. Severe toxicity was not seen in rats treated with EZH1/2 dual inhibitor for 14 days at drug levels higher than those used in the antitumor study. Our results indicate the possibility of EZH1/2 dual inhibitors for clinical applications.

Improvement of Ligand Affinity and Thermodynamic Properties by NMR-Based Evaluation of Local Dynamics and Surface Complementarity in the Receptor-Bound State.

The thermodynamic properties of a ligand in the bound state affect its binding specificity. Strict binding specificity can be achieved by introducing multiple spatially defined interactions, such as hydrogen bonds and van der Waals interactions, into the ligand-receptor interface. These introduced interactions are characterized by restricted local dynamics and improved surface complementarity in the bound state. In this study, we experimentally evaluated the local dynamics and the surface complementarity of weak-affinity ligands in the receptor-bound state by forbidden coherence transfer analysis in free-bound exchange systems (Ex-FCT), using the interaction between a ligand, a myocyte-enhancer factor 2A (MEF2A) docking peptide, and a receptor, p38α, as a model system. The Ex-FCT analyses successfully provided information for the rational design of a ligand with higher affinity and preferable thermodynamic properties for p38α.