Pharmacometabolomics in Early-Phase Clinical Development

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Integrated pharmacokinetics and pharmacometabolomics to reveal the synergistic mechanism of a multicomponent Chinese patent medicine, Mailuo Shutong pills against thromboangiitis obliterans

BACKGROUND

Mailuo Shutong Pills (MLST) have displayed pharmacological activity against thromboangiitis obliterans (TAO). However, the active ingredients and therapeutic mechanism of MLST against TAO remained to be further clarified.

PURPOSE

The aim of this study was to explore the active components of MLST and their synergistic mechanism against TAO by integrating pharmacokinetics (PK) and pharmacometabolomics (PM).

METHODS

TAO model rats were established by sodium laurate solution. Firstly, the efficacy of MLST was evaluated by gangrene score, blood flow velocity, and hematoxylin-eosin (H&E) staining. Secondly, PK research was conducted on bioavailable components to characterize their dynamic behaviors under TAO. Thirdly, multiple plasma and urine metabolic biomarkers for sodium laurate-induced TAO rats were found by untargeted metabolomics, and then variations in TAO-altered metabolites following MLST treatment were analyzed utilizing multivariate and bioinformatic analysis. Additionally, metabolic pathway analysis was performed using MetaboAnalyst. Finally, the dynamic link between absorbed MLST-compounds and TAO-associated endogenous metabolites was established by correlation analysis.

RESULTS

MLST significantly alleviated gangrene symptoms by improving the infiltration of inflammatory cells and blood supply in TAO rats. Significant differences in metabolic profiles were found in 17 differential metabolites in plasma and 24 in urine between Sham and TAO rats. The 10 bioavailable MLST-compounds, such as chlorogenic acid and paeoniflorin, showed positive or negative correlations with various TAO-altered metabolites related to glutamate metabolism, histidine metabolism, arachidonic acid metabolism and so on.

CONCLUSION

This study originally investigated the dynamic interaction between MLST and the biosystem, providing unique insight for disclosing the active components of MLST and their synergistic mechanisms against TAO, which also shed light on new therapeutic targets for TAO and treatment.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Pharmacokinetics and Metabolomic Profiling of Metformin and Andrographis paniculata: A Protocol for a Crossover Randomised Controlled Trial

This protocol aims to profile the pharmacokinetics of metformin and Andrographis paniculata (AP) and continue with untargeted pharmacometabolomics analysis on pre-dose and post-dose samples to characterise the metabolomics profiling associated with the human metabolic pathways. This is a single-centre, open-labelled, three periods, crossover, randomised-controlled, single-dose oral administration pharmacokinetics and metabolomics trial of metformin 1000 mg (n = 18), AP 1000 mg (n = 18), or AP 2000 mg (n = 18) in healthy volunteers under the fasting condition. Subjects will be screened according to a list of inclusion and exclusion criteria. Investigational products will be administered according to the scheduled timeline. Vital signs and adverse events will be monitor periodically, and standardized meals will be provided to the subjects. Fifteen blood samples will be collected over 24 h, and four urine samples will be collected within a 12 h period. Onsite safety monitoring throughout the study and seven-day phone call safety follow-up will be compiled after the last dose of administration. The plasma samples will be analysed for the pharmacokinetics parameters to estimate the drug maximum plasma concentration. Untargeted metabolomic analysis between pre-dose and maximum plasma concentration (Cmax) samples will be performed for metabolomic profiling to identify the dysregulation of human metabolic pathways that link to the pharmacodynamics effects. The metformin arm will focus on the individualised Cmax plasma concentration for metabolomics study and used as a model drug. After this, an investigation of the dose-dependent effects will be performed between pre-dose samples and median Cmax concentration samples in the AP 1000 mg and AP 2000 mg arms for metabolomics study. The study protocol utilises a crossover study design to incorporate a metabolomics-based study into pharmacokinetics trial in the drug development program. The combination analyses will complement the interpretation of pharmacological effects according to the bioavailability of the drug.

Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes

Pharmacometabolomics in early phase clinical trials demonstrate the metabolic profiles of a subject responding to a drug treatment in a controlled environment, whereas pharmacokinetics measure the drug plasma concentration in human circulation. Application of the personalized peak plasma concentration from pharmacokinetics in pharmacometabolomic studies provides insights into drugs’ pharmacological effects through dysregulation of metabolic pathways or pharmacodynamic biomarkers. This proof-of-concept study integrates personalized pharmacokinetic and pharmacometabolomic approaches to determine the predictive pharmacodynamic response of human metabolic pathways for type 2 diabetes. In this study, we use metformin as a model drug. Metformin is a first-line glucose-lowering agent; however, the variation of metabolites that potentially affect the efficacy and safety profile remains inconclusive. Seventeen healthy subjects were given a single dose of 1000 mg of metformin under fasting conditions. Fifteen sampling time-points were collected and analyzed using the validated bioanalytical LCMS method for metformin quantification in plasma. The individualized peak-concentration plasma samples determined from the pharmacokinetic parameters calculated using Matlab Simbiology were further analyzed with pre-dose plasma samples using an untargeted metabolomic approach. Pharmacometabolomic data processing and statistical analysis were performed using MetaboAnalyst with a functional meta-analysis peaks-to-pathway approach to identify dysregulated human metabolic pathways. The validated metformin calibration ranged from 80.4 to 2010 ng/mL for accuracy, precision, stability and others. The median and IQR for Cmax was 1248 (849–1391) ng/mL; AUC_0-infinity was 9510 (7314–10,411) ng·h/mL, and Tmax was 2.5 (2.5–3.0) h. The individualized Cmax pharmacokinetics guided the untargeted pharmacometabolomics of metformin, suggesting a series of provisional predictive human metabolic pathways, which include arginine and proline metabolism, branched-chain amino acid (BCAA) metabolism, glutathione metabolism and others that are associated with metformin’s pharmacological effects of increasing insulin sensitivity and lipid metabolism. Integration of pharmacokinetic and pharmacometabolomic approaches in early-phase clinical trials may pave a pathway for developing targeted therapy. This could further reduce variability in a controlled trial environment and aid in identifying surrogates for drug response pathways, increasing the prediction of responders for dose selection in phase II clinical trials.

Lipid A profiling and metabolomics analysis of paired polymyxin-susceptible and -resistant MDR Klebsiella pneumoniae clinical isolates from the same patients before and after colistin treatment

BACKGROUND

The increased incidence of polymyxin-resistant MDR Klebsiella pneumoniae has become a major global health concern.

OBJECTIVES

To characterize the lipid A profiles and metabolome differences between paired polymyxin-susceptible and -resistant MDR K. pneumoniae clinical isolates.

METHODS

Three pairs of K. pneumoniae clinical isolates from the same patients were examined [ATH 7 (polymyxin B MIC 0.25 mg/L) versus ATH 8 (64 mg/L); ATH 15 (0.5 mg/L) versus ATH 16 (32 mg/L); and ATH 17 (0.5 mg/L) versus ATH 18 (64 mg/L)]. Lipid A and metabolomes were analysed using LC-MS and bioinformatic analysis was conducted.

RESULTS

The predominant species of lipid A in all three paired isolates were hexa-acylated and 4-amino-4-deoxy-l-arabinose-modified lipid A species were detected in the three polymyxin-resistant isolates. Significant metabolic differences were evident between the paired isolates. Compared with their corresponding polymyxin-susceptible isolates, the levels of metabolites in amino sugar metabolism (UDP-N-acetyl-α-d-glucosamine and UDP-N-α-acetyl-d-mannosaminuronate) and central carbon metabolism (e.g. pentose phosphate pathway and tricarboxylic acid cycle) were significantly reduced in all polymyxin-resistant isolates [fold change (FC) > 1.5, P < 0.05]. Similarly, nucleotides, amino acids and key metabolites in glycerophospholipid metabolism, namely sn-glycerol-3-phosphate and sn-glycero-3-phosphoethanolamine, were significantly reduced across all polymyxin-resistant isolates (FC > 1.5, P < 0.05) compared with polymyxin-susceptible isolates. However, higher glycerophospholipid levels were evident in polymyxin-resistant ATH 8 and ATH 16 (FC > 1.5, P < 0.05) compared with their corresponding susceptible isolates.

CONCLUSIONS

To our knowledge, this study is the first to reveal significant metabolic perturbations associated with polymyxin resistance in K. pneumoniae.

Evaluation of 1β-Hydroxylation of Deoxycholic Acid as a Non-Invasive Urinary Biomarker of CYP3A Activity in the Assessment of Inhibition-Based Drug-Drug Interaction in Healthy Volunteers

In this study, we aimed to evaluate the utility of endogenous 1β-hydroxy-deoxycholic acid/total deoxycholic acid ratio (1β-OH-DCA/ToDCA) in spot urine as a surrogate marker of cytochrome P450 3A (CYP3A) activity in the assessment inhibition-based drug-drug interactions in healthy volunteers. This was accomplished through an open-label, three-treatment parallel-arm study in healthy male volunteers from Zimbabwe. Each group received itraconazole (ITZ; 100 mg once daily; n = 10), fluconazole (FKZ; 50 mg once daily; n = 9), or alprazolam (APZ; 1 mg once daily; n = 8) orally. Midazolam (MDZ), dosed orally and intravenously, was used as a comparator to validate the exploratory measures of CYP3A activity and the effects of known inhibitors. Urinary metabolic ratios of 1β-OH-DCA/ToDCA before and after CYP3A inhibitor treatment showed a similar magnitude of inhibitory effects of the three treatments as that measured by oral MDZ clearance. The maximum inhibition effect of a 75% reduction in the 1β-OH-DCA/ToDCA ratio compared to the baseline was achieved in the ITZ group following six once-daily doses of 100 mg. The correlations of the two markers for CYP3A inhibitor treatment were significant (r_s = 0.53, p < 0.01). The half-life of urinary endogenous 1β-OH-DCA/ToDCA was estimated as four days. These results suggested that 1β-OH-DCA/ToDCA in spot urine is a promising convenient, non-invasive, sensitive, and relatively quickly responsive endogenous biomarker that can be used for CYP3A inhibition-based drug-drug interaction in clinical studies.

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Quantitative prediction of hepatic CYP3A activity using endogenous markers in healthy subjects after administration of CYP3A inhibitors or inducers

Accurate prediction of cytochrome P450 (CYP) 3A activity in the early stage of drug development and in clinical practice is important. This study aimed to evaluate the previously constructed CYP3A activity prediction model after administration of CYP3A inhibitors and inducers and to modify the model for better prediction of CYP3A activity. Healthy male subjects received the following study drugs during three study periods: midazolam alone (control phase); midazolam with 200 mg of itraconazole (CYP3A inhibition phase); and midazolam with 150 mg of rifampicin (CYP3A induction phase). We quantified the concentrations of several endogenous CYP3A markers in both urine and plasma using gas chromatography-mass spectrometry. The urinary markers, including 6β-hydroxy (OH)-cortisol/cortisol, 6β-OH-cortisone/cortisone, 16α-OH-dehydroepiandrosterone (DHEA)/DHEA, 16α-OH-androstenedione (A-dione)/A-dione and 7β-OH-DHEA/DHEA, were significantly correlated with midazolam clearance in both the CYP3A inhibition and induction phases. We constructed a statistical prediction model after integrating data from a previous study to predict midazolam clearance as follows: Ln(midazolam clearance) = 2.5545 + 0.3988 × ln(7β-OH-DHEA/DHEA) + 0.1984 × ln(16α-OH-DHEA/DHEA) + 0.5031 × ln(6β-OH-cortisol/cortisol) - 0.1261 [ln(7β-OH-DHEA/DHEA) × ln(6β-OH-cortisol/cortisol)] (r² = 0.75). We suggest that quantitating endogenous markers in vivo coupled with the statistical prediction model may be useful for predicting CYP3A parameters.

Blood-Based Biomarkers of Quinpirole Pharmacology: Cluster-Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood-based biomarkers. As a new approach, we applied cluster-based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brain_ECF ) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D_2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brain_ECF after single and 8-day administration, to be analyzed by cluster-based PK/PD analysis. Multiple concentration-effect relations were observed with potencies ranging from 0.001-383 nM. Many biomarker responses seem to distribute over the blood-brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brain_ECF , and branched-chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster-based PK/PD could describe a systems-response across plasma and brain, thereby identifying potential blood-based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Metabolomics in asthma: where do we stand?

PURPOSE OF REVIEW

Metabolomics has been used to uncover the metabolic signatures of asthma, both for biomarker identification and pathophysiologic mechanisms research. We aimed to review recent advances in this field, published since 2016, and discuss these findings implications to future research and application into clinical practice.

RECENT FINDINGS

Experimental asthma models and clinical studies in both children and adults supported independent metabolic signatures of asthma. Common reported pathways included purine, glycerophospholipid, glutathione, fatty acids, and arginine and proline metabolism. Metabolomics-based studies identified candidate biomarkers related to asthma severity and corticosteroid resistance, and supported the definition of the obesity-related phenotype at the molecular level. A systematic review with meta-analysis and recent prospective studies favored exhaled volatile organic compounds as one of the most promising biomarkers in asthma diagnosis and monitoring.

SUMMARY

Metabolomics has provided unique and novel insights into asthma profiling at the molecular level. Current challenges include procedures standardization and control of potentially confounding variables for external validation. Point-of-care technology developments bring metabolomics closer to clinical practice. In addition to biomarkers identification, relating metabolites to their biologic role will serve as critical foundations for understanding the biology underpinning asthma heterogeneity and for specific-targeted therapies. VIDEO ABSTRACT.

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis

The objective of this review is to explain the science of metabolomics-a science of systems biology that measures and studies endogenous small molecules (metabolites) that are present in a single biological sample-and its application to the diagnosis and treatment of sepsis. In addition, we discuss how discovery through metabolomics can contribute to the development of precision medicine targets for this complex disease state and the potential avenues for those new discoveries to be applied in the clinical environment. A nonsystematic literature review was performed focusing on metabolomics, pharmacometabolomics, and sepsis. Human (adult and pediatric) and animal studies were included. Metabolomics has been investigated in the diagnosis, prognosis, and risk stratification of sepsis, as well as for the identification of drug target opportunities. Metabolomics elucidates a new level of detail when compared with other systems biology sciences, with regard to the metabolites that are most relevant in the pathophysiology of sepsis, as well as highlighting specific biochemical pathways at work in sepsis. Metabolomics also highlights biochemical differences between sepsis survivors and nonsurvivors at a level of detail greater than that demonstrated by genomics, transcriptomics, or proteomics, potentially leading to actionable targets for new therapies. The application of pharmacometabolomics and its integration with other systems pharmacology to sepsis therapeutics could be particularly helpful in differentiating drug responders and nonresponders and furthering knowledge of mechanisms of drug action and response. The accumulated literature on metabolomics suggests it is a viable tool for continued discovery around the pathophysiology, diagnosis and prognosis, and treatment of sepsis in both adults and children, and it provides a greater level of biochemical detail and insight than other systems biology approaches. However, the clinical application of metabolomics in sepsis has not yet been fully realized. Prospective validation studies are needed to translate metabolites from the discovery phase into the clinical utility phase.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.

Intra-Target Microdosing - A Novel Drug Development Approach: Proof of Concept, Safety, and Feasibility Study in Humans

Intra‐Target Microdosing (ITM) is a novel drug development approach aimed at increasing the efficiency of first‐in‐human (FIH) testing of new molecular entities (NMEs). ITM combines intra‐target drug delivery and “microdosing,” the subpharmacological systemic exposure. We hypothesized that when the target tissue is small (about 1/100th of total body mass), ITM can lead to target therapeutic‐level exposure with minimal (microdose) systemic exposure. Each of five healthy male volunteers received insulin microdose into the radial artery or full therapeutic dose intravenously in separate visits. Insulin and glucose levels were similar between systemic administration and ITM administration in the ipsilateral hand, and glucose levels demonstrated a reduction in the ipsilateral hand but not in the contralateral hand. Positron emission tomography (PET) imaging of ¹⁸F‐fluorodeoxyglucose (FDG) uptake demonstrated differences between the ipsilateral and contralateral arms. The procedures were safe and well‐tolerated. Results are consistent with ITM proof‐of‐concept (POC) and demonstrate the ethical, regulatory, and logistical feasibility of the approach.

The Burden of the "False-Negatives" in Clinical Development: Analyses of Current and Alternative Scenarios and Corrective Measures

The “false‐negatives” of clinical development are the effective treatments wrongly determined ineffective. Statistical errors leading to “false‐negatives” are larger than those leading to “false‐positives,” especially in typically underpowered early‐phase trials. In addition, “false‐negatives” are usually eliminated from further testing, thereby limiting the information available on them. We simulated the impact of early‐phase power on economic productivity in three developmental scenarios. Scenario 1, representing the current status quo, assumed 50% statistical power at phase II and 90% at phase III. Scenario 2 assumed increased power (80%), and Scenario 3, increased stringency of alpha (1%) at phase II. Scenario 2 led, on average, to a 60.4% increase in productivity and 52.4% increase in profit. Scenario 3 had no meaningful advantages. Our results suggest that additional costs incurred by increasing the power of phase II studies are offset by the increase in productivity. We discuss the implications of our results and propose corrective measures.

Environmental influences in the etiology of colorectal cancer: the premise of metabolomics

PURPOSE OF REVIEW

In this review we discuss how environmental exposures predominate the etiology of colorectal cancer (CRC). With CRC being a personalized disease influenced by genes and environment, our goal was to explore the role metabolomics can play in identifying exposures, assessing the interplay between co-exposures, and the development of personalized therapeutic interventions.

RECENT FINDINGS

Approximately 10 % of CRC cases can be explained by germ-line mutations, whereas the prevailing majority are caused by an initiating exposure event occurring decades prior to diagnosis. Recent research has shown that dietary metabolites are linked to a procarcinogenic or protective environment in the colon which is modulated by the microbiome. In addition, excessive alcohol has been shown to increase the risk of CRC and is dependent on diet (folate), the response of microbiome, and genetic polymorphisms within the folate and alcohol metabolic pathways. Metabolomics can not only be used to identify this modulation of host metabolism, which could affect the progression of the tumors but also response to targeted therapeutics.

SUMMARY

This review highlights the current understanding of the multifaceted etiology and mechanisms of CRC development but also highlights where the field of metabolomics can contribute to a greater understanding of environmental exposure in CRC.

Integrating Pharmacoproteomics into Early-Phase Clinical Development: State-of-the-Art, Challenges, and Recommendations

Pharmacoproteomics is the study of disease-modifying and toxicity parameters associated with therapeutic drug administration, using analysis of quantitative and temporal changes to specific, predetermined, and select proteins, or to the proteome as a whole. Pharmacoproteomics is a rapidly evolving field, with progress in analytic technologies enabling processing of complex interactions of large number of unique proteins and effective use in clinical trials. Nevertheless, our analysis of clinicaltrials.gov and PubMed shows that the application of proteomics in early-phase clinical development is minimal and limited to few therapeutic areas, with oncology predominating. We review the history, technologies, current usage, challenges, and potential for future use, and conclude with recommendations for integration of pharmacoproteomic in early-phase drug development.