Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.     

A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients

PurposeA situational awareness Bayesian network (SA-BN) approach is developed to improve physicians’ trust in the prediction of radiation outcomes and evaluate its performance for personalized adaptive radiotherapy (pART).Methods118 non-small-cell lung cancer patients with their biophysical features were employed for discovery (n = 68) and validation (n = 50) of radiation outcomes prediction modeling. Patients’ important characteristics identified by radiation experts to predict individual’s tumor local control (LC) or radiation pneumonitis with grade ≥ 2 (RP2) were incorporated as expert knowledge (EK). Besides generating an EK-based naïve BN (EK-NBN), an SA-BN was developed by incorporating the EK features into pure data-driven BN (PD-BN) methods to improve the credibility of LC or / and RP2 prediction. After using area under the free-response receiver operating characteristics curve (AU-FROC) to assess the joint prediction of these outcomes, their prediction performances were compared with a regression approach based on the expert yielded estimates (EYE) penalty and its variants.ResultsIn addition to improving the credibility of radiation outcomes prediction, the SA-BN approach outperformed the EYE penalty and its variants in terms of the joint prediction of LC and RP2. The value of AU-FROC improves from 0.70 (95% CI: 0.54–0.76) using EK-NBN, to 0.75 (0.65–0.82) using a variant of EYE penalty, to 0.83 (0.75–0.93) using PD-BN and 0.83 (0.77–0.90) using SA-BN; with similar trends in the validation cohort.ConclusionsThe SA-BN approach can provide an accurate and credible human–machine interface to gain physicians’ trust in clinical decision-making, which has the potential to be an important component of pART.     

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.     

Comparison of Preoperative Alpha-blockade for Resection of Paraganglioma and Pheochromocytoma

ObjectivePhenoxybenzamine (nonselective, noncompetitive alpha-blocker) is the preferred drug for preoperative treatment of pheochromocytoma, but doxazosin (selective, competitive alpha-blocker) may be equally effective. We compared the efficacy of doxazosin vs phenoxybenzamine.MethodsWe conducted a prospective study of patients undergoing pheochromocytoma or paraganglioma resection by randomizing pretreatment with phenoxybenzamine or doxazosin at a single tertiary referral center. The high cost of phenoxybenzamine led to high crossover to doxazosin. Randomization was halted, and a consecutive historical cohort of phenoxybenzamine patients was included for a case-control study design. The efficacy of alpha-blockade was assessed with preinduction infusion of incremental doses of phenylephrine. The primary outcomes were mortality, cardiovascular complications, and intensive care unit admission. The secondary outcomes were hemodynamic instability index (proportion of operation outside of hemodynamic goals), adequacy of blockade by the phenylephrine titration test, and drug costs.ResultsTwenty-four patients were prospectively enrolled (doxazosin, n = 20; phenoxybenzamine, n = 4), and 15 historical patients treated with phenoxybenzamine were added (total phenoxybenzamine, n = 19). No major cardiovascular complications occurred in either group. The phenylephrine dose-response curves showed less blood pressure rise in the phenoxybenzamine than in the doxazosin group (linear regression coefficient = 0.008 vs 0.018, P = .01), suggesting better alpha-blockade in the phenoxybenzamine group. The median hemodynamic instability index was 14% vs 13% in the phenoxybenzamine and doxazosin groups, respectively (P = .56). The median highest daily cost of phenoxybenzamine was $442.20 compared to $5.06 for doxazosin.ConclusionPhenoxybenzamine may blunt intraoperative hypertension better than doxazosin, but this difference did not translate to fewer cardiovascular complications and is offset by a considerably increased cost.     

Continuous Glucose Monitoring for Patients with COVID-19 Pneumonia: Initial Experience at a Tertiary Care Center

ObjectivePatients hospitalized with COVID-19 and hyperglycemia require frequent glucose monitoring, usually performed with glucometers. Continuous glucose monitors (CGMs) are common in the outpatient setting but not yet approved for hospital use. We evaluated CGM accuracy, safety for insulin dosing, and CGM clinical reliability in 20 adult patients hospitalized with COVID-19 and hyperglycemia.MethodsStudy patients were fitted with a remotely monitored CGM. CGM values were evaluated against glucometer readings. The CGM sensor calibration was performed if necessary. CGM values were used to dose insulin, without glucometer confirmation.ResultsCGM accuracy against glucometer, expressed as mean absolute relative difference (MARD), was calculated using 812 paired glucometer-CGM values. The aggregate MARD was 10.4%. For time in range and grades 1 and 2 hyperglycemia, MARD was 11.4%, 9.4%, and 9.1%, respectively, with a small variation between medical floors and intensive care units. There was no MARD correlation with mean arterial blood pressure levels, oxygen saturation, daily hemoglobin levels, and glomerular filtration rates. CGM clinical reliability was high, with 99.7% of the CGM values falling within the “safe” zones of Clarke error grid. After CGM placement, the frequency of glucometer measurements decreased from 5 to 3 and then 2 per day, reducing nurse presence in patient rooms and limiting viral exposure.ConclusionWith twice daily, on-demand calibration, the inpatient CGM use was safe for insulin dosing, decreasing the frequency of glucometer fingersticks. For glucose levels >70 mg/dL, CGMs showed adequate accuracy, without interference from vital and laboratory values.     

Levothyroxine Monotherapy: What Works Better for the Individual With Hypothyroidism?

ObjectiveI explore objective data not supporting the addition of liothyronine (medication) (LT3) to levothyroxine (medication) (LT4) in patients with hypothyroidism. Accurate identification of patients with symptomatic (almost exclusively overt) hypothyroidism is important in evaluating clinical outcomes of therapies. Recent studies have documented that nearly a third of individuals who are offered thyroid hormone are euthyroid at the time of initiation. Additionally, others are clinically diagnosed without biochemical confirmation, so a sizable proportion of those started on LT4 are not hypothyroid. The assumption that nonhypothyroid symptoms will resolve with LT4 is problematic. The true underlying cause of these symptoms remains unidentified and untreated.MethodsIn a narrative fashion I will review the positive predictive value of and correlation of symptoms consistent with hypothyroidism and confirmed hypothyroidism likely to favorably respond to thyroid hormone replacement.ResultsFollowing a review of the reliability of thyroid-stimulating hormone (TSH) in predicting a euthyroid state, the correlation of circulating triiodothyronine (serum measurement) (T3) levels with symptoms and predictive value of T3 to forecast the outcome of adding LT3 to LT4 will be reviewed. The utility of striving for high, middle, or low TSH set points within the expected range to predict changes in clinical quality of life and the ability of blinded patients to sense subtle differences along this spectrum will be documented. In addition, the clinical impact of single nucleotide polymorphisms in the type 2 deiodinase gene will be reviewed. Finally, the overall satisfaction of selected patients with their thyroid hormone treatments will be outlined and preferences for T3-containing treatments from blinded studies will be summarized.ConclusionBasing thyroid hormone treatment decisions on patient symptoms likely results in missed diagnoses We should encourage primary care physicians to assess a differential diagnosis, exclude other diagnoses, and not assume a thyroid etiology when TSH is normal. Modifying treatment to a particular TSH target or adjusting based on a low T3 level does not seem to enhance patient outcomes. Finally, pending further trials of “symptomatic” participants, using sustained release LT3 to mimic normal physiology, and including monocarboxylate 10 transporter and Type 2 deiodinase polymorphisms and objective outcomes, I will continue to depend on therapy with LT4 monotherapy and seek alternative explanations for my patients’ nonspecific symptoms.