Estimation of confidence intervals for quantitation of coeluted peaks in liquid chromatography-Photodiode array detection through a combination of multivariate curve resolution-alternating least-square and Bayesian inference techniques.

There is a dramatic increase in drug candidates that exhibit complex structures and do not comply with Lipinski's rule of five. One of the most critical and complex technical challenges in the quality control of such drug candidates is the control of analogous substances contained in active pharmaceutical ingredients and related formulations. Although the development of ultrahigh-performance liquid chromatography and high-performance columns has improved efficiency per unit time, the difficulty of peak separation to quantify impurities with similar structures and physicochemical properties continues to rise, and so does the probability of failure to achieve the necessary separation. Coeluting peaks observed in the case of high-performance liquid chromatography (HPLC) with photodiode array detection can be separated using the multivariate curve resolution-alternating least-square (MCR-ALS) method exploiting differences in analyte UV spectra. However, relatively large quantitation errors have been observed for coeluting analogous substances, and the reliability of the corresponding quantitative data requires improvement. Herein, Bayesian inference is applied to separation by the MCR-ALS method to develop an algorithm assigning a confidence interval to the quantitative data of each analogous substance. The usefulness and limitations of this approach are tested using two analogs of telmisartan as models. For this test, a simulated two-component HPLC-UV dataset with an intensity ratio (relative to the main peak) of 0.1-1.0 and a resolution of 0.5-1.0 is used. The developed algorithm allows the prediction confidence interval, including the true value, to be assigned to the peak area in almost all cases, even when the intensity ratio, resolution, and signal-to-noise ratio are changed. Finally, the developed algorithm is also evaluated on a real HPLC-UV dataset to confirm that reasonable prediction confidence intervals including true values are assigned to peak areas. In addition to allowing the separation and quantitation of substances such as impurities challenging to separate by HPLC in a scientifically valid manner, which is impossible for conventional HPLC-UV detection, our method can assign confidence intervals to quantitative data. Therefore, the adopted approach is expected to resolve the issues associated with assessing impurities in the quality control of pharmaceuticals.

Algorithm for the early prediction of drug stability using bayesian inference and multiple measurements: Application for predicting the stability of silodosin tablets.

The stability of active pharmaceutical ingredients (APIs) and formulations has become a major chemistry, manufacturing, and control (CMC) concern in the pharmaceutical industry because it can determine the feasibility of research and development, the development period, and the development costs of a certain formulation. To streamline the research and development of pharmaceutical products and create useful pharmaceutical products at an early stage, a technology that predicts the stability of formulations at an early stage and with a high degree of accuracy is needed. When predicting the stability of a substance, highly reliable data are required; however, the stability data are affected by analytical variations that depend on the experimenter, measurement device, and conditions used. Although these variations greatly affect the prediction accuracy, a stability prediction method that considers these variations has not yet been developed. Here, short-term stability data under accelerated conditions were obtained at three institutions using silodosin tablets as a model sample. By combining Bayesian inference with the temporal change in the amount of the main degradation products obtained and the conventional humidity-corrected Arrhenius equation, we developed a new algorithm that provides a narrow confidence interval, even when using data with variations. By using this algorithm and setting an appropriate number of conditions, we were able to obtain a valid confidence intervals in a short period of time. Here, by performing more measurements than those suggested by the minimum measurement frequency indicated in the guideline specified in the International Council for Harmonisation (ICH) of Technical Requirements for Pharmaceuticals for Human Use, we developed a method that can be used to reasonably predict the long-term stability of the drugs, even if the data measurement interval is short. Our results will help solve various problems in today's pharmaceutical product development scenario and contribute to worldwide health and welfare.

Photoluminescence Properties of Single-Walled Carbon Nanotubes Influenced by the Tether Length of Reagents with Two Reactive Sites.

The functionalization of single-walled carbon nanotubes (SWNTs) is an effective method for controlling a local band gap, resulting in photoluminescence (PL) in the near-infrared region. Herein, SWNTs were functionalized using a series of bromoalkanes and dibromoalkanes to evaluate the effects of their length on the nanotube PL properties. When bromoalkanes (Cn H2n+1 Br) or dibromoalkanes (Cn H2n Br2 ) with tether lengths of six or more were utilized for six different semiconducting SWNTs, the obtained SWNT adducts exhibited two new PL peaks, whereas dibromoalkanes with tether lengths of 3-5 (Cn H2n Br2 : n=3-5) produced single peaks. Combined with theoretical calculations, the results suggested that the tether length of reagents changes the formation mechanism of functionalized adducts, that is, Cn H2n Br2 (n=3-5) tends to result in kinetic products.

Estimated cloud-top entrainment index explains positive low-cloud-cover feedback.

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks.

Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or "labeled" chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We present a design of a fake chromatogram generator, along with a method for deep learning of peak-picking neural networks. Fake chromatograms were generated by generation of fake peaks, random sampling of peak positions from feature distributions, and merging with real blank sample chromatograms. Information on the exact start and end points, as labeled on the fake chromatograms, were effective for training and evaluating peak-picking neural networks. The peak-picking neural networks constructed herein outperformed conventional peak-picking software and showed comparable performance with that of experienced operators for processing the widely targeted metabolome data. Results of this study indicate that generation of fake chromatograms would be crucial for developing peak-picking neural networks and a key technology for further improvement of peak picking neural networks.

Effect of External Magnetic Fields on Biological Effectiveness of Proton Beams.

PURPOSE: The purpose is to verify experimentally whether application of magnetic fields longitudinal and perpendicular to a proton beam alters the biological effectiveness of the radiation. METHODS AND MATERIALS: Proton beams with linear energy transfer of 1.1 and 3.3 keV/µm irradiated human cancer and normal cells under a longitudinal (perpendicular) magnetic field of BL (BP) = 0, 0.3, or 0.6 T. Cell survival curves were constructed to evaluate the effects of the magnetic fields on the biological effectiveness. The ratio of dose that would result in a survival fraction of 10% without the magnetic field Dwo to the dose with the magnetic field Dw, R10 = Dwo/Dw, was determined for each cell line and magnetic field. RESULTS: For cancer cells exposed to the 1.1- (3.3-) keV/µm proton beams, R10s were increased to 1.10 ± 0.07 (1.11 ± 0.07) and 1.11 ± 0.07 (1.12 ± 0.07) by the longitudinal magnetic fields of BL = 0.3 and 0.6 T, respectively. For normal cells, R10s were increased to 1.13 ± 0.06 (1.17 ± 0.06) and 1.17 ± 0.06 (1.30 ± 0.06) by the BLs. In contrast, R10s were not changed significantly from 1 by the perpendicular magnetic fields of BP = 0.3 and 0.6 T for both cancer and normal cells exposed to 1.1- and 3.3-keV/µm proton beams. CONCLUSIONS: The biological effectiveness of proton beams was significantly enhanced by longitudinal magnetic fields of BL = 0.3 and 0.6 T, whereas the biological effectiveness was not altered by perpendicular magnetic fields of the same strengths. This enhancement effect should be taken into account in magnetic resonance imaging guided proton therapy with a longitudinal magnetic field.

Influence of a perpendicular magnetic field on biological effectiveness of carbon-ion beams.

Purpose: Our previous study revealed that the application of a magnetic field longitudinal to a carbon-ion beam of 0.1 ≤ B//≤ 0.6 T enhances the biological effectiveness of the radiation. The purpose of this study is to experimentally verify whether the application of a magnetic field perpendicular to the beam also alters the biological effectiveness. Methods and materials: Most experimental conditions other than the magnetic field direction were the same as those used in the previous study to allow comparison of their results. Human cancer and normal cells were exposed to low (12 keV/µm) and high (50 keV/µm) linear energy transfer (LET) carbon-ion beams under the perpendicular magnetic fields of B⊥ = 0, 0.15, 0.3, or 0.6 T generated by a dipole magnet. The effects of the magnetic fields on the biological effectiveness were evaluated by clonogenic cell survival. Doses that would result in the survival of 10%, D10s, were determined for the exposures and analyzed using Student's t-tests. Results: For both cancer and normal cells treated by low- and high-LET carbon-ion beams, the D10s measured in the presence of the perpendicular magnetic fields of B⊥ ≥ 0.15 T were not statistically different (p ≫ .05) from the D10s measured in the absence of the magnetic fields, B⊥ = 0 T. Conclusions: Exposure of human cancer and normal cells to the perpendicular magnetic fields of B⊥ ≤ 0.6 T did not alter significantly the biological effectiveness of the carbon-ion beams, unlike the exposure to longitudinal magnetic fields of the same strength. Although the mechanisms underlying the observed results still require further exploration, these findings indicate that the influence of the magnetic field on biological effectiveness of the carbon-ion beam depends on the applied field direction with respect to the beam.

Enhancement of biological effectiveness of carbon-ion beams by applying a longitudinal magnetic field.

Purpose: A magnetic field longitudinal to an ion beam will potentially affect the biological effectiveness of the radiation. The purpose of this study is to experimentally verify the significance of such effects. Methods and materials: Human cancer and normal cell lines were exposed to low (12 keV/µm) and high (50 keV/µm) linear energy transfer (LET) carbon-ion beams under the longitudinal magnetic fields of B// = 0, 0.1, 0.2, 0.3, or 0.6 T generated by a solenoid magnet. The effects of the magnetic fields on the biological effectiveness were evaluated by clonogenic cell survival. Doses that would result in a survival fraction of 10% (D10s) were determined for each cell line and magnetic field. Results: For cancer cells exposed to the low (high)-LET beams, D10 decreased from 5.2 (3.1) Gy at 0 T to 4.3 (2.4) Gy at 0.1 T, while no further decrease in D10 was observed for higher magnetic fields. For normal cells, decreases in D10 of comparable magnitudes were observed by applying the magnetic fields. Conclusions: Significant decreases in D10, i.e. significant enhancements of the biological effectiveness, were observed in both cancer and normal cells by applying longitudinal magnetic fields of B// ≥ 0.1 T. These effects were enhanced with LET. Further studies are required to figure out the mechanism underlying the observed results.

Intelligent peak deconvolution through in-depth study of the data matrix from liquid chromatography coupled with a photo-diode array detector applied to pharmaceutical analysis.

Multivariate curve resolution-alternating least squares (MCR-ALS) method was investigated for its potential to accelerate pharmaceutical research and development. The fast and efficient separation of complex mixtures consisting of multiple components, including impurities as well as major drug substances, remains a challenging application for liquid chromatography in the field of pharmaceutical analysis. In this paper we suggest an integrated analysis algorithm functioning on a matrix of data generated from HPLC coupled with photo-diode array detector (HPLC-PDA) and consisting of the mathematical program for the developed multivariate curve resolution method using an expectation maximization (EM) algorithm with a bidirectional exponentially modified Gaussian (BEMG) model function as a constraint for chromatograms and numerous PDA spectra aligned with time axis. The algorithm provided less than ±1.0% error between true and separated peak area values at resolution (Rs) of 0.6 using simulation data for a three-component mixture with an elution order of a/b/c with similarity (a/b)=0.8410, (b/c)=0.9123 and (a/c)=0.9809 of spectra at peak apex. This software concept provides fast and robust separation analysis even when method development efforts fail to achieve complete separation of the target peaks. Additionally, this approach is potentially applicable to peak deconvolution, allowing quantitative analysis of co-eluted compounds having exactly the same molecular weight. This is complementary to the use of LC-MS to perform quantitative analysis on co-eluted compounds using selected ions to differentiate the proportion of response attributable to each compound.

Madden-Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer.

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden-Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003-2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.

A simple algorithm for beam profile diagnostics using a thermographic camera.

A new algorithm for digital image processing apparatuses is developed to evaluate profiles of high-intensity DC beams from temperature images of irradiated thin foils. Numerical analyses are performed to examine the reliability of the algorithm. To simulate the temperature images acquired by a thermographic camera, temperature distributions are numerically calculated for 20 MeV proton beams with different parameters. Noise in the temperature images which is added by the camera sensor is also simulated to account for its effect. Using the algorithm, beam profiles are evaluated from the simulated temperature images and compared with exact solutions. We find that niobium is an appropriate material for the thin foil used in the diagnostic system. We also confirm that the algorithm is adaptable over a wide beam current range of 0.11-214 µA, even when employing a general-purpose thermographic camera with rather high noise (ΔT(NETD) ≃ 0.3 K; NETD: noise equivalent temperature difference).

(11)CH4 molecule production using a NaBH4 target for (11)C ion acceleration.

Solid-state materials suitable for use as proton irradiation targets were investigated for producing high-purity (11)CH4 molecules for heavy-ion cancer therapy. The radioactivity of gas produced by proton irradiation was measured for several target materials. Also, the radioactive molecular species of the produced gas were analyzed by radio gas chromatography. We found that 5 × 10(12) (11)C molecules could be collected by proton irradiation on a NaBH4 target. We also found that the (11)CH4 molecules were produced and collected directly from the irradiated target, owing to the hydrogen atoms bound in the solid-state NaBH4.

Total synthesis of pseudodehydrothyrsiferol.

An enantioselective total synthesis of pseudodehydrothyrsiferol has been accomplished. The synthetic sequence highlights formation of the highly strained tetrahydropyran C-ring by a Mitsunobu-type S(N)2 reaction with an oxygen nucleophile.

A Madden-Julian oscillation event realistically simulated by a global cloud-resolving model.

A Madden-Julian Oscillation (MJO) is a massive weather event consisting of deep convection coupled with atmospheric circulation, moving slowly eastward over the Indian and Pacific Oceans. Despite its enormous influence on many weather and climate systems worldwide, it has proven very difficult to simulate an MJO because of assumptions about cumulus clouds in global meteorological models. Using a model that allows direct coupling of the atmospheric circulation and clouds, we successfully simulated the slow eastward migration of an MJO event. Topography, the zonal sea surface temperature gradient, and interplay between eastward- and westward-propagating signals controlled the timing of the eastward transition of the convective center. Our results demonstrate the potential making of month-long MJO predictions when global cloud-resolving models with realistic initial conditions are used.