Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data.

Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.

Overview of modern approaches for identifying and evaluating heterogeneous treatment effects from clinical data.

There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D'Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a "black box" that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.

Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Motivation: The identification of biomarkers to support decision-making is central to personalized medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework. Results: Our contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic versus predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1-3 orders of magnitude faster than competitors, making it useful for biomarker discovery in 'big data' scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker. Availability and implementation: R implementations of the suggested methods are available at https://github.com/sechidis. Supplementary information: Supplementary data are available at Bioinformatics online.

Application of structured statistical analyses to identify a biomarker predictive of enhanced tralokinumab efficacy in phase III clinical trials for severe, uncontrolled asthma.

BACKGROUND: Tralokinumab is an anti-interleukin (IL)-13 monoclonal antibody investigated for the treatment of severe, uncontrolled asthma in two Phase III clinical trials, STRATOS 1 and 2. The STRATOS 1 biomarker analysis plan was developed to identify biomarker(s) indicative of IL-13 activation likely to predict tralokinumab efficacy and define a population in which there was an enhanced treatment effect; this defined population was then tested in STRATOS 2. METHODS: The biomarkers considered were blood eosinophil counts, fractional exhaled nitric oxide (FeNO), serum dipeptidyl peptidase-4, serum periostin and total serum immunoglobulin E. Tralokinumab efficacy was measured as the reduction in annualised asthma exacerbation rate (AAER) compared with placebo (primary endpoint measure of STRATOS 1 and 2). The biomarker analysis plan included negative binomial and generalised additive models, and the Subgroup Identification based on Differential Effect Search (SIDES) algorithm, supported by robustness and sensitivity checks. Effects on the key secondary endpoints of STRATOS 1 and 2, which included changes from baseline in standard measures of asthma outcomes, were also investigated. Prior to the STRATOS 1 read-out, numerous simulations of the methodology were performed with hypothetical data. RESULTS: FeNO and periostin were identified as the only biomarkers potentially predictive of treatment effect, with cut-offs chosen by the SIDES algorithm of > 32.3 ppb and > 27.4 ng/ml, respectively. The FeNO > 32.3 ppb subgroup was associated with greater AAER reductions and improvements in key secondary endpoints compared with the periostin > 27.4 ng/ml subgroup. Upon further evaluation of AAER reductions at different FeNO cut-offs, ≥37 ppb was chosen as the best cut-off for predicting tralokinumab efficacy. DISCUSSION: A rigorous statistical approach incorporating multiple methods was used to investigate the predictive properties of five potential biomarkers and to identify a participant subgroup that demonstrated an enhanced tralokinumab treatment effect. Using STRATOS 1 data, our analyses identified FeNO at a cut-off of ≥37 ppb as the best assessed biomarker for predicting enhanced treatment effect to be tested in STRATOS 2. Our findings were inconclusive, which reflects the complexity of subgroup identification in the severe asthma population. TRIAL REGISTRATION: STRATOS 1 and 2 are registered on ClinicalTrials.gov ( NCT02161757 registered on June 12, 2014, and NCT02194699 registered on July 18, 2014).

Study specific prediction intervals for random-effects meta-analysis: A tutorial: Prediction intervals in meta-analysis.

The pooled estimate of the average effect is of primary interest when fitting the random-effects model for meta-analysis. However estimates of study specific effects, for example those displayed on forest plots, are also often of interest. In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random-effects model. These estimates can be accompanied by prediction intervals that indicate a plausible range of study specific true effects. We coalesce and elucidate the available literature and illustrate the methodology using two published meta-analyses as examples. We also perform a simulation study that reveals that coverage probability of study specific prediction intervals are substantially too low if the between-study variance is small but not negligible. Researchers need to be aware of this defect when interpreting prediction intervals. We also show how empirical Bayes estimates, accompanied with study specific prediction intervals, can embellish forest plots. We hope that this tutorial will serve to provide a clear theoretical underpinning for this methodology and encourage its widespread adoption.

Efficient synthesis of a long carbohydrate chain alkyl glycoside catalyzed by cyclodextrin glycosyltransferase (CGTase).

Alkyl glycosides with long carbohydrate groups are surfactants with attractive properties but they are very difficult to synthesize. Here, a method for extension of the carbohydrate group of commercially available dodecyl-beta-d-maltoside (DDM) is presented. DDM was converted to dodecyl-beta-d-maltooctaoside (DDMO) in a single step by using a CGTase as catalyst and alpha-cyclodextrin (alpha-CD) as glycosyl donor. The coupling reaction is under kinetic control and the maximum yield depends on the selectivity of the enzyme. The Bacillus macerans CGTase favored the coupling reaction while the Thermoanaerobacter enzyme also catalyzed disproportionation reactions leading to a broader product range. A high ratio alpha-CD/DDM favored a high yield of DDMO and yields up to 80% were obtained using the B. macerans enzyme as catalyst.

Whole-genome analysis reveals a strong positional bias of conserved dMyc-dependent E-boxes.

Myc is a transcription factor with diverse biological effects ranging from the control of cellular proliferation and growth to the induction of apoptosis. Here we present a comprehensive analysis of the transcriptional targets of the sole Myc ortholog in Drosophila melanogaster, dMyc. We show that the genes that are down-regulated in response to dmyc inhibition are largely identical to those that are up-regulated after dMyc overexpression and that many of them play a role in growth control. The promoter regions of these targets are characterized by the presence of the E-box sequence CACGTG, a known dMyc binding site. Surprisingly, a large subgroup of (functionally related) dMyc targets contains a single E-box located within the first 100 nucleotides after the transcription start site. The relevance of this E-box and its position was confirmed by a mutational analysis of a selected dMyc target and by the observation of its evolutionary conservation in a different Drosophila species, Drosophila pseudoobscura. These observations raise the possibility that a subset of Myc targets share a distinct regulatory mechanism.

Melanin-concentrating hormone receptor 1 deficiency increases insulin sensitivity in obese leptin-deficient mice without affecting body weight.

The hypothalamic peptide melanin-concentrating hormone (MCH) plays important roles in energy homeostasis. Animals overexpressing MCH develop hyperphagia, obesity, and insulin resistance. In this study, mice lacking both the MCH receptor-1 (MCHr1 knockout) and leptin (ob/ob) double-null mice (MCHr1 knockout ob/ob) were generated to investigate whether the obesity and/or the insulin resistance linked to the obese phenotype of ob/ob mice was attenuated by ablation of the MCHr1 gene. In MCHr1 knockout ob/ob mice an oral glucose load resulted in a lower blood glucose response and markedly lower insulin levels compared with the ob/ob mice despite no differences in body weight, food intake, or energy expenditure. In addition, MCHr1 knockout ob/ob mice had higher locomotor activity and lean body mass, lower body fat mass, and altered body temperature regulation compared with ob/ob mice. In conclusion, MCHr1 is important for insulin sensitivity and/or secretion via a mechanism not dependent on decreased body weight.

A novel variant of Thermotoga neapolitana beta-glucosidase B is an efficient catalyst for the synthesis of alkyl glucosides by transglycosylation.

Alkyl glycosides are surfactants with good biodegradability and low toxicity, attractive to produce by an enzymatic method to get a well-defined product. In this paper, we report a novel thermostable variant of a family 3 beta-glucosidase to be an efficient catalyst in alkyl-glucoside forming reactions using transglycosylation with hexanol or octanol as the acceptor molecule. The enzyme has an apparent optimum for hydrolysis at 90 degrees C, which coincides with its unfolding temperature. The total activity is lower at lower temperature (60 degrees C), but the ratio of alcoholysis/hydrolysis is slightly more favourable. This ratio is however more heavily influenced by the water content and the pH. Optimal reaction conditions for hexyl glucoside synthesis from p-nitrophenyl-beta-glucopyranoside were a water/hexanol two-phase system containing 16% (v/v) water, pH 5.8, and a temperature of 60 degrees C. Under these conditions, the total initial reaction rate was 153 micromol min(-1)mg(-1) and the alcoholysis/hydrolysis ratio was 5.1. Comparing with alcoholysis/hydrolysis ratios of other beta-glycosidases, TnBgl3B can be considered to be a very promising catalyst for alkyl glucoside production.

Characterisation of a glycosylated alkyl polyglycoside produced by a cyclodextrin glycosyltransferase by HPLC-ELSD and -MS.

A transglycosylation reaction between an alkyl polyglycoside and α-cyclodextrin catalysed by cyclodextrin glycosyltransferase (CGTase) from Bacillus macerans was investigated. The reaction products were identified by comparison with standards generated by CGTase catalysed modification of pure alkyl glycosides using HPLC-ELSD and -MS analysis. The main products were alkyl glucopyranosides (substrates present in the alkyl polyglycoside) glycosylated with 6 (primary coupling products) or 12 (secondary coupling products) glucose residues. Both α and ß anomers were glycosylated.

Substrate complexation and aggregation influence the cyclodextrin glycosyltransferase (CGTase) catalyzed synthesis of alkyl glycosides.

Bacillus macerans cyclodextrin glycosyltransferase (CGTase) was used to convert dodecyl-ß-maltoside (DDM) to dodecyl-ß-maltooctaoside (DDMO) using α-cyclodextrin (α-CD) or starch as glycosyl donors. At 300mM α-CD, varied DDM concentration and 60°C, the reaction obeyed Michaelis-Menten kinetics with a K(m) value of 18mM and a V(max) value of 100U/mg enzyme. However, at 25mM α-CD the reaction rate decreased with increasing DDM concentration (5-50mM), and when the α-CD concentration was varied at fixed DDM concentration an S shaped curve was obtained. The deviations from Michaelis-Menten kinetics were interpreted as being caused by formation of inclusion complexes between α-CD and DDM and by micellation of DDM. To achieve a high reaction rate, a high concentration of free α-CD is necessary, since α-CD in the form of a complex has low reactivity. When starch is used as glycosyl donor in the CGTase catalyzed alkyl glycoside elongation reaction, it is thus important to choose reaction conditions under which the cyclization of starch to α-CD is efficient.

A novel method to assess gastric accommodation and peristaltic motility in conscious rats.

OBJECTIVE: To simultaneously study gastric accommodation and peristaltic motility in the whole stomach of conscious rats by measuring intragastric pressure (IGP) during test-meal infusion. MATERIAL AND METHODS: After an overnight fast, a test-meal infusion system and a catheter to measure IGP were connected to a chronically implanted gastric fistula. IGP was measured during infusion of an X-ray-opaque, non-nutritious viscous test meal (0.25-2 ml min(-1)); gastric motility and emptying were assessed by X-ray fluoroscopy. Peristaltic motility-induced IGP waves were quantified as a motility index (wave amplitude divided by wavelength). Experiments were performed in Sprague-Dawley (SD) rats and in the high-anxiety Wistar Kyoto (WKY) rats. Moreover, the effects of 30 mg kg(-1) NG-nitro-L-arginine methyl ester (L-NAME), 1 mg kg(-1) atropine or 20 mg kg(-1) molsidomine were tested in SD rats. RESULTS: Compared with SD rats, IGP increased significantly faster during stomach distension in WKY rats, indicating impaired accommodation in the latter strain. Motility indices did not differ between the two strains. L-NAME significantly increased IGP during stomach distension, indicating decreased gastric accommodation. However, no change in motility indices was observed with L-NAME. Treatment with atropine significantly increased IGP and decreased motility indices, indicating decreased gastric accommodation and motility. Molsidomine significantly decreased IGP during stomach distension but did not affect motility. The results correspond to X-ray observations, and confirm literature data. CONCLUSIONS: We conclude that IGP measurement during test-meal infusion represents an efficient and novel method to compare gastric accommodation and peristaltic motility in the whole stomach of conscious rats.

Pharmacological analysis of components of the change in transmural potential difference evoked by distension of rat proximal small intestine in vivo.

The reflex response to distension of the small intestine in vivo is complex and not well understood. The aim of this study was to characterize the neural mechanisms contributing to the complex time course of the intestinal secretory response to distension. Transmucosal potential difference (PD) was used as a marker for mucosal chloride secretion, which reflects the activity of the secretomotor neurons. Graded distensions (5, 10, and 20 mmHg) of distal rat duodenum with saline for 5 min induced a biphasic PD response with an initial peak (rapid response) followed by a plateau (sustained response). The rapid response was significantly reduced by the neural blockers tetrodotoxin and lidocaine (given serosally) and by intravenous (iv) administration of the ganglionic blocker hexamethonium and the NK(1) receptor antagonist SR-140333. Serosal TTX and iv SR-140333 significantly reduced the sustained response, which was also reduced by the NK(3) receptor antagonist talnetant and by the vasoactive intestinal polypeptide (VPAC) receptor antagonist [4Cl-d-Phe(6), Leu(17)]-VIP. Serosal lidocaine and iv hexamethonium had no significant effect on this component. Inhibition of nitric oxide synthase had no effect on any of the components of the PD response to distension. The PD response to distension thus seems to consist of two components, a rapidly activating and adapting component operating via nicotinic transmission and NK(1) receptors, and a slow component operating via VIP-ergic transmission and involving both NK(1) and NK(3) receptors.