Parsing interindividual drug variability: an emerging role for systems pharmacology

Understanding the impact of ABCG2 polymorphisms on drug pharmacokinetics: focus on rosuvastatin and allopurinol

INTRODUCTION

In addition to the well-established understanding of the pharmacogenetics of drug-metabolizing enzymes, there is growing data on the effects of genetic variation in drug transporters, particularly ATP-binding cassette (ABC) transporters. However, the evidence that these genetic variants can be used to predict drug effects and to adjust individual dosing to avoid adverse events is still limited.

AREAS COVERED

This review presents a summary of the current literature from the PubMed database as of February 2024 regarding the impact of genetic variants on ABCG2 function and their relevance to the clinical use of the HMG-CoA reductase inhibitor rosuvastatin and the xanthine oxidase inhibitor allopurinol.

EXPERT OPINION

Although there are pharmacogenetic guidelines for the ABCG2 missense variant Q141K, there is still some conflicting data regarding the clinical benefits of these recommendations. Some caution appears to be warranted in homozygous ABCG2 Q141K carriers when rosuvastatin is administered at higher doses and such information is already included in the drug label. The benefit of dose adaption to lower possible side effects needs to be evaluated in prospective clinical studies.

Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

Being precise with anticoagulation to reduce adverse drug reactions: are we there yet?

Anticoagulants are potent therapeutics widely used in medical and surgical settings, and the amount spent on anticoagulation is rising. Although warfarin remains a widely prescribed oral anticoagulant, prescriptions of direct oral anticoagulants (DOACs) have increased rapidly. Heparin-based parenteral anticoagulants include both unfractionated and low molecular weight heparins (LMWHs). In clinical practice, anticoagulants are generally well tolerated, although interindividual variability in response is apparent. This variability in anticoagulant response can lead to serious incident thrombosis, haemorrhage and off-target adverse reactions such as heparin-induced thrombocytopaenia (HIT). This review seeks to highlight the genetic, environmental and clinical factors associated with variability in anticoagulant response, and review the current evidence base for tailoring the drug, dose, and/or monitoring decisions to identified patient subgroups to improve anticoagulant safety. Areas that would benefit from further research are also identified. Validated variants in VKORC1, CYP2C9 and CYP4F2 constitute biomarkers for differential warfarin response and genotype-informed warfarin dosing has been shown to reduce adverse clinical events. Polymorphisms in CES1 appear relevant to dabigatran exposure but the genetic studies focusing on clinical outcomes such as bleeding are sparse. The influence of body weight on LMWH response merits further attention, as does the relationship between anti-Xa levels and clinical outcomes. Ultimately, safe and effective anticoagulation requires both a deeper parsing of factors contributing to variable response, and further prospective studies to determine optimal therapeutic strategies in identified higher risk subgroups.

Lack of exposure to pharmacogenomics education among the health care providing students in the West Bank of Palestine

OBJECTIVES

Evaluating the knowledge in pharmacogenomics (PGx) is the first step toward the implementation of PGx testing in clinical practice. This survey aimed to evaluate the knowledge of PGx testing among healthcare providing students at the top-ranked university in the West Bank of Palestine.

METHODS

First an online questionnaire consisting of 30 questions regarding the demographic, knowledge, and attitude toward pharmacogenomics testing was structured and validated. Then the questionnaire was distributed to 1,000 current students from different fields.

RESULTS

696 responses was received. The results showed that almost half of the participants (n=355, 51.1%) have never took any courses about PGx during their university training. Only 81 (11.7%) of the students who took the PGx course stated that it helped them understanding how genetic variations affect drug response. The majority of the students were uncertain (n=352, 50.6%) or disagreed (n=143, 20.6%) that the lectures during university education described the effects of genetic variants on drug response. Although most of the students (70-80%) answered that genetic variants can indeed affect the drug's response, only 162 students (23.3%) responded that VKORC1 and CYP2C9 genotypes influence the response to warfarin. In addition, only 94 (13.5%) students were aware that many medicine labels include clinical information about PGx testing provided by the FDA.

CONCLUSIONS

It is concluded from the results of this survey that there is a lack of exposure to PGx education associated with poor knowledge of PGx testing among the healthcare providing students in the West Bank of Palestine. It is recommended to include and improve the lectures and courses regarding PGx as this will have a major impact on precision medicine.

Drug dosage modifications in 24 million in-patient prescriptions covering eight years: A Danish population-wide study of polypharmacy

Polypharmacy has generally been assessed by raw counts of different drugs administered concomitantly to the same patients; not with respect to the likelihood of dosage-adjustments. To address this aspect of polypharmacy, the objective of the present study was to identify co-medications associated with more frequent dosage adjustments. The data foundation was electronic health records from 3.2 million inpatient admissions at Danish hospitals (2008-2016). The likelihood of dosage-adjustments when two drugs were administered concomitantly were computed using Bayesian logistic regressions. We identified 3,993 co-medication pairs that associate significantly with dosage changes when administered together. Of these pairs, 2,412 (60%) did associate with readmission, mortality or longer stays, while 308 (8%) associated with reduced kidney function. In comparison to co-medications pairs that were previously classified as drug-drug interactions, pairs not classified as drug-drug interactions had higher odds ratios of dosage modifications than drug pairs with an established interaction. Drug pairs not corresponding to known drug-drug interactions while still being associated significantly with dosage changes were prescribed to fewer patients and mentioned more rarely together in the literature. We hypothesize that some of these pairs could be associated with yet to be discovered interactions as they may be harder to identify in smaller-scale studies.

Interethnic differences in drug response: projected impact of genetic variations in the Saudi population

Ethnicity is known to have an impact on drug responses. This is particularly important for drugs that have a narrow therapeutic window, nonlinearity in pharmacokinetics and are metabolized by enzymes that demonstrate genetic polymorphisms. However, most clinical trials are conducted among Caucasians, which might limit the usefulness of the findings of such studies for other ethnicities. The representation of participants from Saudi Arabia in global clinical trials is low. Therefore, there is a paucity of evidence to assess the impact of ethnic variability in the Saudi population on drug response. In this article, the authors assess the projected impact of genetic polymorphisms in drug-metabolizing enzymes and drug targets on drug response in the Saudi population.

Interpretable deep learning for improving cancer patient survival based on personal transcriptomes

Precision medicine chooses the optimal drug for a patient by considering individual differences. With the tremendous amount of data accumulated for cancers, we develop an interpretable neural network to predict cancer patient survival based on drug prescriptions and personal transcriptomes (CancerIDP). The deep learning model achieves 96% classification accuracy in distinguishing short-lived from long-lived patients. The Pearson correlation between predicted and actual months-to-death values is as high as 0.937. About 27.4% of patients may survive longer with an alternative medicine chosen by our deep learning model. The median survival time of all patients can increase by 3.9 months. Our interpretable neural network model reveals the most discriminating pathways in the decision-making process, which will further facilitate mechanistic studies of drug development for cancers.

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

BACKGROUND

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

METHODS

This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods.

RESULTS

We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models.

CONCLUSIONS

The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.

Decoding kinase-adverse event associations for small molecule kinase inhibitors

Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application “Identification of Kinase-Specific Signal” (https://gongj.shinyapps.io/ml4ki). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective.

Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. Here, the authors employ a machine-learning model to examine the relationships between kinase targets and adverse events in the trials of 16 FDA-approved SMKIs.

The Role of miRNAs in Dexmedetomidine's Neuroprotective Effects against Brain Disorders

There are limited neuroprotective strategies for various central nervous system conditions in which fast and sustained management is essential. Neuroprotection-based therapeutics have become an intensively researched topic in the neuroscience field, with multiple novel promising agents, from natural products to mesenchymal stem cells, homing peptides, and nanoparticles-mediated agents, all aiming to significantly provide neuroprotection in experimental and clinical studies. Dexmedetomidine (DEX), an α2 agonist commonly used as an anesthetic adjuvant for sedation and as an opioid-sparing medication, stands out in this context due to its well-established neuroprotective effects. Emerging evidence from preclinical and clinical studies suggested that DEX could be used to protect against cerebral ischemia, traumatic brain injury (TBI), spinal cord injury, neurodegenerative diseases, and postoperative cognitive disorders. MicroRNAs (miRNAs) regulate gene expression at a post-transcriptional level, inhibiting the translation of mRNA into functional proteins. In vivo and in vitro studies deciphered brain-related miRNAs and dysregulated miRNA profiles after several brain disorders, including TBI, ischemic stroke, Alzheimer's disease, and multiple sclerosis, providing emerging new perspectives in neuroprotective therapy by modulating these miRNAs. Experimental studies revealed that some of the neuroprotective effects of DEX are mediated by various miRNAs, counteracting multiple mechanisms in several disease models, such as lipopolysaccharides induced neuroinflammation, β-amyloid induced dysfunction, brain ischemic-reperfusion injury, and anesthesia-induced neurotoxicity models. This review aims to outline the neuroprotective mechanisms of DEX in brain disorders by modulating miRNAs. We address the neuroprotective effects of DEX by targeting miRNAs in modulating ischemic brain injury, ameliorating the neurotoxicity of anesthetics, reducing postoperative cognitive dysfunction, and improving the effects of neurodegenerative diseases.

Role of Pharmacogenomics in Individualizing Treatment for Alzheimer's Disease

The development of Alzheimer's disease therapeutics has been challenging, with 99% of clinical trials failing to find a significant difference between drug and placebo. While the quest continues for more effective treatments, there is emerging evidence that pharmacogenetic considerations are important factors in regard to metabolism, efficacy, and toxicity of drugs. Currently, there are five US Food and Drug Administration-approved drugs for the treatment of Alzheimer's disease; three acetylcholinesterase inhibitors, memantine, and aducanumab. Introducing a limited genetic panel consisting of APOE4, CYP2D6*10, and BChE*K would optimize acetylcholinesterase inhibitor therapy, facilitate immunotherapy risk assessment, and inform an amyloid-related imaging abnormality surveillance schedule. In view of the genetic heterogeneity of Alzheimer's disease identified in genome-wide association studies, pharmacogenetics is expected to play an increasing role in mechanism-specific treatment strategies and personalized medicine.

A Comparison of Oral vs Intravenous Tranexamic Acid in Patients Undergoing Staggered Bilateral Total Knee Arthroplasty

BACKGROUND

All previous studies comparing the blood sparing efficacy oral and intravenous tranexamic acid (TXA) in total knee arthroplasty have involved two or more patient cohorts, outcomes of which may be limited by inter-individual variability in human drug response. The purpose of this study was to evaluate if both oral and intravenous preparations of TXA are equivalent at reducing blood loss in the same patients undergoing staggered bilateral total knee arthroplasty.

METHODS

40 patients undergoing staggered bilateral total knee replacement were recruited. They received 2 g of oral TXA 2 hours preoperatively for the first knee and 1 g of bolus intravenous TXA 15 minutes before skin incision for the second knee. 7 patients were excluded for protocol deviation, leaving 33 participants for the study. The second knee was operated within 5-6 days of the first knee. The primary outcome was reduction in hemoglobin. Equivalence was tested with a two one-sided test (TOST) and a P < 0.05 indicated equivalence between oral and intravenous modes of TXA administration.

RESULTS

The mean reduction in hemoglobin was similar between oral and intravenous mode of TXA administration (2.18 and 2.16 g/dl respectively, P<0.0001, equivalence). There was no significant difference in the total hemoglobin loss and total red blood cell volume loss {(104 and 102 g, P=0.86) and (865 and 863 ml, P=0.53) respectively}.

CONCLUSION

Oral and intra venous TXA have equal blood sparing properties in patients undergoing staggered bilateral total knee arthroplasty.

Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Inherent Dangers of Using Non-US Food and Drug Administration-Approved Substances of Abuse

Use of US Food and Drug Administration-approved substances of abuse has innate risks due to pharmacologic and pharmacokinetic properties of the medications, but the risk when using nonapproved drug products is much greater. Unbeknownst to the user, the dose of active ingredients in substances of abuse can vary substantially between different products because of manufacturing practices or improper storage. Even naturally occurring substances of abuse can have extensive dosage variability because of effects of the growing season and conditions, or differences in harvesting, storage, or manufacture of the finished products. Many illicit substances are adulterated, to make up for intentional underdosing or to enhance the effect of the intended active ingredient. These adulterants can be dangerous and produce direct cardiovascular, neurologic, hematologic, or dermatologic reactions or obscure adverse effects. Finally, an illicit substance can be contaminated or substituted for another one during its manufacture, leading to differences in adverse events, adverse event severity, or the drug interaction profile. Substances can be contaminated with microbes that induce infections or heavy metals that can damage organs or cause cancer. This milieu of undisclosed substances can also induce drug interactions. For reasons that are discussed, individuals who use substances of abuse are at increased risk of morbidity or mortality if they develop coronavirus disease 2019. Health professionals who treat patients with acute, urgent events associated with substances of abuse, or those treating the chronic manifestations of addiction, need to appreciate the complex and variable composition of substances of abuse and their potential health effects.

Quantitative Investigation of Irinotecan Metabolism, Transport, and Gut Microbiome Activation

The anticancer drug irinotecan shows serious dose-limiting gastrointestinal toxicity regardless of intravenous dosing. Although enzymes and transporters involved in irinotecan disposition are known, quantitative contributions of these mechanisms in complex in vivo disposition of irinotecan are poorly understood. We explained intestinal disposition and toxicity of irinotecan by integrating 1) in vitro metabolism and transport data of irinotecan and its metabolites, 2) ex vivo gut microbial activation of the toxic metabolite SN-38, and 3) the tissue protein abundance data of enzymes and transporters relevant to irinotecan and its metabolites. Integration of in vitro kinetics data with the tissue enzyme and transporter abundance predicted that carboxylesterase (CES)-mediated hydrolysis of irinotecan is the rate-limiting process in the liver, where the toxic metabolite formed is rapidly deactivated by glucuronidation. In contrast, the poor SN-38 glucuronidation rate as compared with its efficient formation by CES2 in the enterocytes is the key mechanism of the intestinal accumulation of the toxic metabolite. The biliary efflux and organic anion transporting polypeptide-2B1-mediated enterocyte uptake can also synergize buildup of SN-38 in the enterocytes, whereas intestinal P-glycoprotein likely facilitates SN-38 detoxification in the enterocytes. The higher SN-38 concentration in the intestine can be further nourished by β-d-glucuronidases. Understanding the quantitative significance of the key metabolism and transport processes of irinotecan and its metabolites can be leveraged to alleviate its intestinal side effects. Further, the proteomics-informed quantitative approach to determine intracellular disposition can be extended to determine susceptibility of cancer cells over normal cells for precision irinotecan therapy. SIGNIFICANCE STATEMENT: This work provides a deeper insight into the quantitative relevance of irinotecan hydrolysis (activation), conjugation (deactivation), and deconjugation (reactivation) by human or gut microbial enzymes or transporters. The results of this study explain the characteristic intestinal exposure and toxicity of irinotecan. The quantitative tissue-specific in vitro to in vivo extrapolation approach presented in this study can be extended to cancer cells.

ABCB1, ABCG2, ABCC1, ABCC2, and ABCC3 drug transporter polymorphisms and their impact on drug bioavailability: what is our current understanding?

INTRODUCTION

Interindividual differences in drug response are a frequent clinical challenge partly due to variation in pharmacokinetics. ATP-binding cassette (ABC) transporters are crucial determinants of drug disposition. They are subject of gene regulation and drug-interaction; however, it is still under debate to which extend genetic variants in these transporters contribute to interindividual variability of a wide range of drugs.

AREAS COVERED

This review discusses the current literature on the impact of genetic variants in ABCB1, ABCG2 as well as ABCC1, ABCC2, and ABCC3 on pharmacokinetics and drug response. The aim was to evaluate if results from recent studies would increase the evidence for potential clinically relevant pharmacogenetic effects.

EXPERT OPINION

Although enormous efforts have been made to investigate effects of ABC transporter genotypes on drug pharmacokinetics and response, the majority of studies showed only weak if any associations. Despite few unique results, studies mostly failed to confirm earlier findings or still remained inconsistent. The impact of genetic variants on drug bioavailability is only minor and other factors regulating the transporter expression and function seem to be more critical. In our opinion, the findings on the so far investigated genetic variants in ABC efflux transporters are not suitable as predictive biomarkers.

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response-Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end-of-phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response-guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA-sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight

Rosuvastatin is a frequently used probe to study transporter‐mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time‐concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293‐transfected cells, and developed a semimechanistic model with a bottom‐up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two‐fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (C_max; 76%; AFE: 1.05), and terminal half‐life (t_1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.

Hepatocytic transcriptional signatures predict comparative drug interaction potential of rifamycin antibiotics

Current strategies to treat tuberculosis (TB) and co-morbidities involve multidrug combination therapies. Rifamycin antibiotics are a key component of TB therapy and a common source of drug–drug interactions (DDIs) due to induction of drug metabolizing enzymes (DMEs). Management of rifamycin DDIs are complex, particularly in patients with co-morbidities, and differences in DDI potential between rifamycin antibiotics are not well established. DME profiles induced in response to tuberculosis antibiotics (rifampin, rifabutin and rifapentine) were compared in primary human hepatocytes. We identified rifamycin induced DMEs, cytochrome P450 (CYP) 2C8/3A4/3A5, SULT2A, and UGT1A4/1A5 and predicted lower DDIs of rifapentine with 58 clinical drugs used to treat co-morbidities in TB patients. Transcriptional networks and upstream regulator analyses showed FOXA3, HNF4α, NR1I2, NR1I3, NR3C1 and RXRα as key transcriptional regulators of rifamycin induced DMEs. Our study findings are an important resource to design effective medication regimens to treat common co-conditions in TB patients.

Weight Loss after 12 Weeks of Exercise and/or Nutritional Guidance Is Not Obligatory for Induced Changes in Local Fat/Lean Mass Indexes in Adults with Excess of Adiposity

The objectives of this secondary analysis are (1) to investigate the differential effects of exercise training modalities-high-intensity interval training (HIIT), resistance training (RT), combined training (CT = HIIT + RT), and/or nutritional guidance (NG) alone-on local fat/lean mass indexes in adults with excess of adiposity; (2) to identify the individual patterns of response based on either a clinical criterion of weight loss (≥5%) and/or technical error (TE) of measurement of local fat/lean mass indexes; and (3) to assess the individual change for body composition parameters assigned either to HIIT, RT, CT, and/or NG groups utilizing a TE. A 12-week trial was conducted in 55 participants randomized to one of the four interventions. The primary outcome was clinical change in body weight (i.e., weight loss of ≥5%). Secondary outcomes included change in ratio of android and gynoid fat mass, as well as local fat and lean mass indexes (arms, trunk, and legs), before and after intervention. The main findings from the current analysis revealed that (i) after 12 weeks of follow-up, significant decreases in several body composition indexes were found including body weight, arm, trunk, and legs fat mass, and android and gynecoid fat mass were observed in HIIT, RT, and CT groups (p < 0.05); (ii) a significant proportion of individuals showed a positive response following 12 weeks of training, led by the HIIT group with 44% and followed by RT with 39% in 9 indexes; (iii) the HIIT group showed lowest rates of adverse responders with (6%); and (iv) the individual patterns of response utilizing clinically meaningful weight loss were not necessarily associated with the corresponding individual training-induced changes in body composition indexes in adults with excess of adiposity. Overall, the study suggests that HIIT has an important ability to reduce the prevalence of non-response to improve body composition indexes.

An ex vivo multiparametric flow cytometry assay using human whole blood to simultaneously measure cytotoxicity and leishmanicidal activities

Therapeutic options for the treatment of leishmaniasis are insufficient and need improvements owing to their low efficiency and high toxicity as well as the emergence of resistant strains. The limited number of new drugs for neglected diseases and lack of innovation in your development are still challenges. In this context, the process of discovery and development of biological assays play a pivotal role for the identification of bioactive compounds. The assays currently used for screening of drugs with cytotoxic activity against Leishmania parasites, include different processes that utilize intact parasite (free or intracellular) or specific enzymes of metabolism as a target cell. These assays allow the screening of large numbers of samples followed by more detailed secondary confirmatory assays to confirm the observed activity and assess their toxicity. In the present study, we described the development of a new functional and more complete assay that enables simultaneous assessment of potential anti-Leishmania compounds through evaluation of internalization of fluorescein-labeled L. braziliensis promastigotes by human peripheral blood monocytes and their cytotoxicity by flow cytometry. We standardized the conditions for parasite labeling to achieve better phagocytosis analysis by setting the ratio of number of parasites per cell as 1 to 2, at incubation time of 6h. The cytotoxicity assessment was performed by the quantification of cells undergoing early/late apoptosis and necrosis using a double labelling platform employing 7AAD for late apoptosis and necrosis analysis and Annexin-V for early apoptosis evaluation. Hemolysis analysis was an additional parameter to test cytotoxicity. Two drugs used on clinic (Amphotericin B and Glucantime®) were used to validate the proposed methodology, and the assay was able to detect their known leishmanicidal activity and immunotoxicity properties. This new predictive assay will contribute to the development of translational medicine strategies in drug discovery for neglected diseases such as leishmaniasis.

Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR

INTRODUCTION

Pre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective.

METHODS AND ANALYSIS

The EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment.

ETHICS AND DISSEMINATION

The protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences.

TRIAL REGISTRATION NUMBER

German Clinical Trials Register, DRKS00013909.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Prevalence of responders for hepatic fat, adiposity and liver enzyme levels in response to a lifestyle intervention in children with overweight/obesity: EFIGRO randomized controlled trial

BACKGROUND/OBJECTIVE

Exercise and lifestyle interventions have been shown to reduce hepatic fat (HF) and adiposity in youth. However, the interindividual response in HF after a lifestyle intervention with or without exercise in children is unknown. The aim of the present study was to compare interindividual variability for HF, adiposity, gamma-glutamyl transferase (GGT), and the aspartate aminotransferase to alanine aminotransferase ratio (AST/ALT) in children with overweight/obesity participating in a 22-week lifestyle intervention with (intensive intervention) or without exercise (control intervention).

METHODS

Data from 102 children (9-12 years, 55% girls) with overweight/obesity participating in the EFIGRO randomized controlled trial were analyzed. Percentage HF (magnetic resonance imaging), weight, body and fat mass index (BMI and FMI), GGT, AST/ALT, cardiorespiratory fitness (CRF, 20 meters shuttle run test) were assessed before and after the intervention by the same trained researchers. The control intervention consisted in 11 sessions of a family-based lifestyle and psycho-educational program. The intensive intervention included the control intervention plus supervised exercise (3 sessions/week).

RESULTS

The prevalence of responders for HF (54% vs. 34%), weight (27% vs. 11%), BMI (71% vs. 47%), FMI (90% vs. 60%), and GGT (69% vs. 39%) was higher in the intensive than in the control group (Ps < 0.05). Responders for weight (16 ± 3 vs. 6 ± 2 laps) and BMI (11 ± 2 vs. 3 ± 4 laps) improved more CRF levels than non-responders (Ps < 0.05).

CONCLUSIONS

The addition of exercise to a lifestyle intervention may increase the responder rates for HF, adiposity, and GGT in children with overweight/obesity. Improvements in CRF may explain differences between weight and BMI responders and non-responders.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02258126.

Multiscale modelling of drug transport and metabolism in liver spheroids

In early preclinical drug development, potential candidates are tested in the laboratory using isolated cells. These in vitro experiments traditionally involve cells cultured in a two-dimensional monolayer environment. However, cells cultured in three-dimensional spheroid systems have been shown to more closely resemble the functionality and morphology of cells in vivo. While the increasing usage of hepatic spheroid cultures allows for more relevant experimentation in a more realistic biological environment, the underlying physical processes of drug transport, uptake and metabolism contributing to the spatial distribution of drugs in these spheroids remain poorly understood. The development of a multiscale mathematical modelling framework describing the spatio-temporal dynamics of drugs in multicellular environments enables mechanistic insight into the behaviour of these systems. Here, our analysis of cell membrane permeation and porosity throughout the spheroid reveals the impact of these properties on drug penetration, with maximal disparity between zonal metabolism rates occurring for drugs of intermediate lipophilicity. Our research shows how mathematical models can be used to simulate the activity and transport of drugs in hepatic spheroids and in principle any organoid, with the ultimate aim of better informing experimentalists on how to regulate dosing and culture conditions to more effectively optimize drug delivery.

The effects of fasting on drug metabolism

Introduction: There is considerable variability in the rates and extent of drug metabolism between patients due to physiological, genetic, pharmacologic, environmental and nutritional factors such as fasting. This variability in drug metabolism may result in treatment failure or, conversely, in increased side effects or toxicity. Preclinical studies have shown that fasting alters drug metabolism by modulating the activity of drug metabolizing enzymes involved. However, until recently little was known about the effects of fasting on drug metabolism in humans.Areas covered: This review describes the effects of fasting on drug metabolism based on both preclinical studies and studies performed in humans.Expert opinion: A better understanding of the effects of fasting may improve the efficacy and safety of pharmacotherapy for individual patients. Fasting contributes to variability in human drug metabolism by differentially affecting drug metabolizing enzymes. Although the effects of fasting on drug metabolism appear to be small (between 10-20%), fasting may be relevant for drugs with a small therapeutic range and/or in combination with other factors that contribute to variability in drug metabolism such as physiological, genetic or pharmacological factors. Therefore, additional research on this topic is warranted.

High Content Solid Dispersions for Dose Window Extension: A Basis for Design Flexibility in Fused Deposition Modelling

PURPOSE

This study uses high drug content solid dispersions for dose window extension beyond current demonstrations using fused deposition modelling (FDM) to; i) accommodate pharmaceutically relevant doses of drugs of varying potencies at acceptable dosage form sizes and ii) enable enhanced dose flexibility via modular dosage form design concepts.

METHODS

FDM was used to generate ~0.5 mm thick discs of varying diameter (2-10 mm) from melt-extruded feedstocks based on 10% to 50% w/w felodipine in ethyl cellulose. Drug content was determined by UV spectroscopy and dispensing precision from printed disc mass.

RESULTS

Mean felodipine content was within ±5% of target values for all print volumes and compositions including contents as high as ~50% w/w. However, poor dispensing precision was evident at all print volumes.

CONCLUSIONS

In pursuit of dose flexibility, this successful demonstration of dose window extension using high content solid dispersions preserves FDM design flexibility by maintaining applicability to drugs of varying potencies. The achieved uniformity of content supports the application of varying content solid dispersions to modular dosage form concepts to enhance dose flexibility. However, poor dispensing precision impedes its utilisation until appropriate compatibility between FDM hardware and materials at varying drug contents can be attained.

Single nucleotide polymorphisms in treatment of polycystic ovary syndrome: a systematic review

As 15-20% of reproductive aged females are suffering from polycystic ovary syndrome (PCOS), a large number of pharmacological preparations are frequently available in the market for the treatment of PCOS; however, they seem to be ineffective and cause undesirable side effects. This has emphasized the need to optimize dosage regimens for individualized treatment. The objective of this systematic review is to review single nucleotide polymorphisms (SNPs) associated with drugs used for the treatment of PCOS to understand pharmacogenetics variability of patients to drug response there by helping clinicians in designing tailored treatments and possibly reducing adverse drug reactions. A comprehensive electronic literature search was conducted to highlight some clinically relevant SNPs that act to influence PCOS and associated co-morbidities. A total of 16 studies were included in this review. These genetic variations can be used as a potential target for pharmacotherapy and pharmacogenetic clinical trials for better diagnosis, management, and treatment planning.

Dry eye disease immune responses and topical therapy

There is accumulating evidence that inflammation is one of the key components of dry eye because chronic ocular surface inflammation can be both a result as well as an initiator of dry eye. The need for continuing anti-inflammatory therapy may be determined in part by the extent that non-modifiable factors such as gender and age-related aqueous or lipid or mucus production deficiencies contribute to its chronicity. This perspective examines how the need for increased dosage of a topical anti-inflammatory drug may be determined by the degree of difficulty that a topically administered drug has in accessing different sites of tear deficiency and associated inflammation.

Working together to deliver stratified medicine research effectively

INTRODUCTION OR BACKGROUND

Stratified medicine is an important area of research across all clinical specialties, with far reaching impact in many spheres. Despite recently formulated global policy and research programmes, major challenges for delivering stratified medicine studies persist. Across the globe, clinical research infrastructures have been setup to facilitate high quality clinical research.

SOURCES OF DATA

This article reviews the literature and summarizes views collated from a workshop held by the UK Pharmacogenetics and Stratified Medicine Network and the NIHR Clinical Research Network in November 2016.

AREAS OF AGREEMENT

Stratified medicine is an important area of clinical research and health policy, benefitting from substantial international, cross-sector investment and has the potential to transform patient care. However there are significant challenges to the delivery of stratified medicine studies.

AREAS OF CONTROVERSY

Complex methodology and lack of consistency of definition and agreement on key approaches to the design, regulation and delivery of research contribute to these challenges and would benefit from greater focus.

GROWING POINTS

Effective partnership and development of consistent approaches to the key factors relating to stratified medicine research is required to help overcome these challenges.

AREAS TIMELY FOR DEVELOPING RESEARCH

This paper examines the critical contribution clinical research networks can make to the delivery of national (and international) initiatives in the field of stratified medicine. Importantly, it examines the position of clinical research in stratified medicine at a time when pressures on the clinical and social services are mounting.

Statistical Considerations for Exercise Protocols Aimed at Measuring Trainability

The individual response to exercise training is of great interest with methods that have been proposed to measure this response reviewed in this paper. However, individual training response estimates may be biased by various sources of variability present in exercise studies, and in particular by within-subject variability. We propose the use of protocols that can separate trainability from within-subject variability.

A human population-based organotypic in vitro model for cardiotoxicity screening

Assessing inter-individual variability in responses to xenobiotics remains a substantial challenge, both in drug development with respect to pharmaceuticals and in public health with respect to environmental chemicals. Although approaches exist to characterize pharmacokinetic variability, there are no methods to routinely address pharmacodynamic variability. In this study, we aimed to demonstrate the feasibility of characterizing inter-individual variability in a human in vitro model. Specifically, we hypothesized that genetic variability across a population of iPSC-derived cardiomyocytes translates into reproducible variability in both baseline phenotypes and drug responses. We measured baseline and drug-related effects in iPSC-derived cardiomyocytes from 27 healthy donors on kinetic Ca²⁺ flux and high-content live cell imaging. Cells were treated in concentration-response with cardiotoxic drugs: isoproterenol (β-adrenergic receptor agonist/positive inotrope), propranolol (β-adrenergic receptor antagonist/negative inotrope), and cisapride (hERG channel inhibitor/QT prolongation). Cells from four of the 27 donors were further evaluated in terms of baseline and treatment-related gene expression. Reproducibility of phenotypic responses was evaluated across batches and time. iPSC-derived cardiomyocytes exhibited reproducible donor-specific differences in baseline function and drug-induced effects. We demonstrate the feasibility of using a panel of population-based organotypic cells from healthy donors as an animal replacement experimental model. This model can be used to rapidly screen drugs and chemicals for inter-individual variability in cardiotoxicity. This approach demonstrates the feasibility of quantifying inter-individual variability in xenobiotic responses and can be expanded to other cell types for which in vitro populations can be derived from iPSCs.

Application of Microphysiological Systems to Enhance Safety Assessment in Drug Discovery

Enhancing the early detection of new therapies that are likely to carry a safety liability in the context of the intended patient population would provide a major advance in drug discovery. Microphysiological systems (MPS) technology offers an opportunity to support enhanced preclinical to clinical translation through the generation of higher-quality preclinical physiological data. In this review, we highlight this technological opportunity by focusing on key target organs associated with drug safety and metabolism. By focusing on MPS models that have been developed for these organs, alongside other relevant in vitro models, we review the current state of the art and the challenges that still need to be overcome to ensure application of this technology in enhancing drug discovery.

Dynamic Brains and the Changing Rules of Neuroplasticity: Implications for Learning and Recovery

A growing number of research publications have illustrated the remarkable ability of the brain to reorganize itself in response to various sensory experiences. A traditional view of this plastic nature of the brain is that it is predominantly limited to short epochs during early development. Although examples showing that neuroplasticity exists outside of these finite time-windows have existed for some time, it is only recently that we have started to develop a fuller understanding of the different regulators that modulate and underlie plasticity. In this article, we will provide several lines of evidence indicating that mechanisms of neuroplasticity are extremely variable across individuals and throughout the lifetime. This variability is attributable to several factors including inhibitory network function, neuromodulator systems, age, sex, brain disease, and psychological traits. We will also provide evidence of how neuroplasticity can be manipulated in both the healthy and diseased brain, including recent data in both young and aged rats demonstrating how plasticity within auditory cortex can be manipulated pharmacologically and by varying the quality of sensory inputs. We propose that a better understanding of the individual differences that exist within the various mechanisms that govern experience-dependent neuroplasticity will improve our ability to harness brain plasticity for the development of personalized translational strategies for learning and recovery following brain injury or disease.

Natural diversity facilitates the discovery of conserved chemotherapeutic response mechanisms

Organismal fitness depends on adaptation to complex niches where chemical compounds and pathogens are omnipresent. These stresses can lead to the fixation of alleles in both xenobiotic responses and proliferative signaling pathways that promote survival in these niches. However, both xenobiotic responses and proliferative pathways vary within and among species. For example, genetic differences can accumulate within populations because xenobiotic exposures are not constant and selection is variable. Additionally, neutral genetic variation can accumulate in conserved proliferative pathway genes because these systems are robust to genetic perturbations given their essential roles in normal cell-fate specification. For these reasons, sensitizing mutations or chemical perturbations can disrupt pathways and reveal cryptic variation. With this fundamental view of how organisms respond to cytotoxic compounds and cryptic variation in conserved signaling pathways, it is not surprising that human patients have highly variable responses to chemotherapeutic compounds. These different responses result in the low FDA-approval rates for chemotherapeutics and underscore the need for new approaches to understand these diseases and therapeutic interventions. Model organisms, especially the classic invertebrate systems of Caenorhabditis elegans and Drosophila melanogaster, can be used to combine studies of natural variation across populations with responses to both xenobiotic compounds and chemotherapeutics targeted to conserved proliferative signaling pathways.

The Pharmacogenomic and Metabolomic Predictors of ACE Inhibitor and Angiotensin II Receptor Blocker Effectiveness and Safety

Hypertension (HTN) is the most common chronic disease in the USA. Hypertensive patients frequently require repeat primary care visits to find an effective drug or drug combination to control their disease. Currently, patients are prescribed drugs for HTN based on race, age, and comorbidities and although the current guidelines are reasonable starting points for prescribing, 50% of hypertensive patients still fail to achieve target blood pressures. Despite numerous strategies to improve compliance, drug effectiveness, and optimization of initial drug choice, effectiveness has remained largely unchanged over the past two decades. Therefore, it is important to pursue alternative strategies to more effectively treat patients and to decrease medical costs. Additional precision medicine work is needed to identify factors associated with effectiveness of commonly used antihypertensive medications. The objective of this manuscript is to present a comprehensive review of the pharmacogenomic and metabolomic factors associated with ACEI and ARB effectiveness and safety.

Global Proteomic Analysis of Human Liver Microsomes: Rapid Characterization and Quantification of Hepatic Drug-Metabolizing Enzymes

Many genetic and environmental factors lead to interindividual variations in the metabolism and transport of drugs, profoundly affecting efficacy and toxicity. Precision dosing, that is, targeting drug dose to a well characterized subpopulation, is dependent on quantitative models of the profiles of drug-metabolizing enzymes (DMEs) and transporters within that subpopulation, informed by quantitative proteomics. We report the first use of ion mobility-mass spectrometry for this purpose, allowing rapid, robust, label-free quantification of human liver microsomal (HLM) proteins from distinct individuals. Approximately 1000 proteins were identified and quantified in four samples, including an average of 70 DMEs. Technical and biological variabilities were distinguishable, with technical variability accounting for about 10% of total variability. The biological variation between patients was clearly identified, with samples showing a range of expression profiles for cytochrome P450 and uridine 5'-diphosphoglucuronosyltransferase enzymes. Our results showed excellent agreement with previous data from targeted methods. The label-free method, however, allowed a fuller characterization of the in vitro system, showing, for the first time, that HLMs are significantly heterogeneous. Further, the traditional units of measurement of DMEs (pmol mg^-1 HLM protein) are shown to introduce error arising from variability in unrelated, highly abundant proteins. Simulations of this variability suggest that up to 1.7-fold variation in apparent CYP3A4 abundance is artifactual, as are background positive correlations of up to 0.2 (Spearman correlation coefficient) between the abundances of DMEs. We suggest that protein concentrations used in pharmacokinetic predictions and scaling to in vivo clinical situations (physiologically based pharmacokinetics and in vitro-in vivo extrapolation) should be referenced instead to tissue mass.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.