Comparison of the Efficacy of Levothyroxine Suppression Dose Computed Based on Actual Body Weight vs. Lean Body Mass among Differentiated Thyroid Cancer Patients: A Randomized Controlled Trial.

BACKGROUND: The dose of levothyroxine (LT4) after total thyroidectomy is usually computed based on actual body weight. However, metabolism through deiodination of thyroid hormones usually occur in the lean body compartment. An optimal dose to reduce delay in achieving target levels is essential to improve quality of life, reduce risk factors and cost. OBJECTIVE: Comparison of the efficacy of two methods of computation for the initial levothyroxine dose in patients with differentiated thyroid cancer based on actual body weight vs. lean body mass in achieving thyroid-stimulating hormone (TSH) goals. METHODOLOGY: Randomized, single-center, 12-week open label controlled trial among adult patients with differentiated thyroid cancer post total thyroidectomy who underwent radioactive therapy at St. Luke's Medical Center Quezon City from July-December 2018. Participants were divided into 2 groups - Actual Body Weight (ABW) and Lean Body Mass (LBM). Levothyroxine dose was computed based on ABW vs. LBM and TSH determined at 6th and 12th weeks after. RESULTS: 52 participants (ABW n=26; LBM n=26) were included. ABW group had significantly higher mean LT4 dosage (2.2 mcg/kg) compared to the LBM group (1.4 mcg/kg) (p-value<0.001). ABW group had lower TSH levels at 6th week (5.7 uIU/mL) than LBM group (18.4 uIU/mL) but the difference was not significant. (p-value=0.064). A significantly lower TSH level was observed at week 12 in the ABW group (1.6 uIU/mL) compared to the LBM group (3.8 uIU/mL) (p-value=0.010). However, both methods were not associated with achievement of TSH goal at 6th and 12th week (p-value=0.512 and 0.780, respectively). CONCLUSION: Among patients with differentiated thyroid cancer who underwent 1st time RAI therapy, ABW method of computation for LT4 dosage is better compared to the LBM method due to the lower TSH trend seen at 6th week and statistically significantly lower mean TSH at week 12, although, both method of computations did not achieve target TSH levels at the 6th nor 12th week.

Comprehensive identification of protein disulfide bonds with pepsin/trypsin digestion, Orbitrap HCD and Spectrum Identification Machine.

Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools. Here we present a new protocol tailored toward mapping SS; it uses readily available reagents, instruments, and software. For sample preparation, a 4-h pepsin digestion at pH 1.3 followed by an overnight trypsin digestion at pH 6.5 can maximize the release of SS-containing peptides from non-reduced proteins, while minimizing SS scrambling. For LC/MS/MS analysis, SS-containing peptides can be efficiently fragmented with HCD in a Q Exactive Orbitrap mass spectrometer, preserving SS for subsequent identification. Our bioinformatics protocol describes how we tailored our freely downloadable and easy-to-use software, Spectrum Identification Machine for Cross-Linked Peptides (SIM-XL), to minimize false identification and facilitate manual validation of SS-peptide mass spectra. To substantiate this optimized method, we've comprehensively identified 14 out of 17 known SS in BSA. SIGNIFICANCE: Comprehensive and accurate identification of SS in proteins is critical for elucidating protein structures and functions. Yet, it is far from routine to accomplish this task in many analytical or core laboratories. Numerous published methods require complex sample preparation methods, specialized mass spectrometers and cumbersome or proprietary software tools, thus cannot be easily implemented in unspecialized laboratories. Here, we describe a robust and rapid SS mapping approach that utilizes readily available reagents, instruments, and software; it can be easily implemented in any analytical core laboratories, and tested for its impact on the research community.