A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans.

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans.Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells. However, this requires estimating patient-specific parameters of the model from clinical data, which is a challenging inverse problem due to limited temporal data and the limited time between imaging and diagnosis. This work proposes a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters of a reaction-diffusion PDE model of GBM growth from a single 3D structural MRI snapshot. PINNs embed both the data and the PDE into a loss function, thus integrating theory and data. Key innovations include the identification and estimation of characteristic non-dimensional parameters, a pre-training step that utilizes the non-dimensional parameters and a fine-tuning step to determine the patient specific parameters. Additionally, the diffuse domain method is employed to handle the complex brain geometry within the PINN framework. Our method is validated both on synthetic and patient datasets, and shows promise for real-time parametric inference in the clinical setting for personalized GBM treatment.

Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma.

Bispecific T cell engagers (TCEs) are an emerging anti-cancer modality that redirects cytotoxic T cells to tumor cells expressing tumor-associated antigens (TAAs), thereby forming immune synapses to exert anti-tumor effects. Designing pharmacokinetically acceptable TCEs and optimizing their size presents a considerable protein engineering challenge, particularly given the complexity of intercellular bridging between T cells and tumor cells. Therefore, a physiologically-relevant and clinically-verified computational modeling framework is of crucial importance to understand the protein engineering trade-offs. In this study, we developed a quantitative, physiologically-based computational framework to predict immune synapse formation for a variety of molecular formats of TCEs in tumor tissues. Our model incorporates a molecular size-dependent biodistribution using the two-pore theory, extravasation of T cells and hematologic cancer cells, mechanistic bispecific intercellular binding of TCEs, and competitive inhibitory interactions by shed targets. The biodistribution of TCEs was verified by positron emission tomography imaging of [89Zr]AMG211 (a carcinoembryonic antigen-targeting TCE) in patients. Parameter sensitivity analyses indicated that immune synapse formation was highly sensitive to TAA expression, degree of target shedding, and binding selectivity to tumor cell surface TAAs over shed targets. Notably, the model suggested a "sweet spot" for TCEs' CD3 binding affinity, which balanced the trapping of TCEs in T-cell-rich organs. The final model simulations indicated that the number of immune synapses is similar (~55/tumor cell) between two distinct clinical stage B cell maturation antigen (BCMA)-targeting TCEs, PF-06863135 in an IgG format and AMG420 in a BiTE format, at their respective efficacious doses in multiple myeloma patients. This result demonstrates the applicability of the developed computational modeling framework to molecular design optimization and clinical benchmarking for TCEs, thus suggesting that this framework can be applied to other targets to provide a quantitative means to facilitate model-informed best-in-class TCE discovery and development.

Applications of Quantitative System Pharmacology Modeling to Model-Informed Drug Development.

Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed.

Functional characterization of novel rare CYP2A6 variants and potential implications for clinical outcomes.

CYP2A6 activity, phenotyped by the nicotine metabolite ratio (NMR), is a predictor of several smoking behaviors, including cessation and smoking-related disease risk. The heritability of the NMR is 60-80%, yet weighted genetic risk scores (wGRSs) based on common variants explain only 30-35%. Rare variants (minor allele frequency <1%) are hypothesized to explain some of this missing heritability. We present two targeted sequencing studies where rare protein-coding variants are functionally characterized in vivo, in silico, and in vitro to examine this hypothesis. In a smoking cessation trial, 1687 individuals were sequenced; characterization measures included the in vivo NMR, in vitro protein expression, and metabolic activity measured from recombinant proteins. In a human liver bank, 312 human liver samples were sequenced; measures included RNA expression, protein expression, and metabolic activity from extracted liver tissue. In total, 38 of 47 rare coding variants identified were novel; characterizations ranged from gain-of-function to loss-of-function. On a population level, the portion of NMR variation explained by the rare coding variants was small (~1%). However, upon incorporation, the accuracy of the wGRS was improved for individuals with rare protein-coding variants (i.e., the residuals were reduced), and approximately one-third of these individuals (12/39) were re-assigned from normal to slow metabolizer status. Rare coding variants can alter an individual's CYP2A6 activity; their integration into wGRSs through precise functional characterization is necessary to accurately assess clinical outcomes and achieve precision medicine for all. Investigation into noncoding variants is warranted to further explain the missing heritability in the NMR.

Does sex alter the relationship between CYP2B6 variation, hydroxybupropion concentration and bupropion-aided smoking cessation in African Americans? A moderated mediation analysis.

BACKGROUND AND AIMS: CYP2B6, a genetically variable enzyme, converts bupropion to its active metabolite hydroxybupropion. CYP2B6 activity and bupropion-aided cessation differ between women and men. The aim of this study was to determine whether genetically normal (versus reduced) CYP2B6 activity increases bupropion-aided cessation in African American smokers via higher hydroxybupropion concentration, and whether this differs by sex. DESIGN AND SETTING: Secondary analysis of a smoking cessation clinical trial (NCT00666978). PARTICIPANTS/CASES: African American light smokers (≤ 10 cigarettes/day). INTERVENTIONS: Participants were treated with bupropion for 7 weeks. MEASUREMENTS: Participants with detectable bupropion and/or hydroxybupropion concentrations were divided into normal (n = 64) and reduced (n = 109) CYP2B6 activity groups based on the presence of decreased-function CYP2B6*6 and CYP2B6*18 alleles. Biochemically verified smoking cessation was assessed at week 3, end of treatment (7 weeks) and follow-up (26 weeks). FINDINGS: Normal (versus reduced) CYP2B6 activity was associated with increased cessation at week 7, which was mediated by higher hydroxybupropion concentration [odds ratio (OR) = 1.25, 95% confidence interval (CI) = 1.03, 1.78]; this mediation effect persisted at week 26 (OR = 1.23, 95% CI = 1.02, 1.70). The mediation effect was similar in women (n = 116; OR = 1.33, 95% CI = 1.01, 2.30) and men (n = 57; OR = 1.33, 95% CI = 0.92, 3.87). Moreover, sex did not appear to moderate the mediation effect, although this should be tested in a larger sample. CONCLUSIONS: In African American light smokers with verified early bupropion use, genetically normal CYP2B6 activity appears to be indirectly associated with greater smoking cessation success in a relationship mediated by higher hydroxybupropion concentration. The mediating effect of higher hydroxybupropion concentration on smoking cessation persists beyond the active treatment phase and does not appear to differ by sex.

Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective.

The predictive performance of physiologically-based pharmacokinetics (PBPK) models for pharmacokinetics (PK) in renal impairment (RI) and hepatic impairment (HI) populations was evaluated using clinical data from 29 compounds with 106 organ impairment study arms were collected from 19 member companies of the International Consortium for Innovation and Quality in Pharmaceutical Development. Fifty RI and 56 HI study arms with varying degrees of organ insufficiency along with control populations were evaluated. For RI, the area under the curve (AUC) ratios of RI to healthy control were predicted within twofold of the observed ratios for > 90% (N = 47/50 arms). For HI, > 70% (N = 43/56 arms) of the hepatically impaired to healthy control AUC ratios were predicted within twofold. Inaccuracies, typically overestimation of AUC ratios, occurred more in moderate and severe HI. PBPK predictions can help determine the need and timing of organ impairment study. It may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance. PBPK modeling may be used to support mild impairment study waivers or clinical study design.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Preclinical Antitumor Activity and Biodistribution of a Novel Anti-GCC Antibody-Drug Conjugate in Patient-derived Xenografts.

Guanylyl cyclase C (GCC) is a unique therapeutic target with expression restricted to the apical side of epithelial cell tight junctions thought to be only accessible by intravenously administered agents on malignant tissues where GCC expression is aberrant. In this study, we sought to evaluate the therapeutic potential of a second-generation investigational antibody-dug conjugate (ADC), TAK-164, comprised of a human anti-GCC mAb conjugated via a peptide linker to the highly cytotoxic DNA alkylator, DGN549. The in vitro binding, payload release, and in vitro activity of TAK-164 was characterized motivating in vivo evaluation. The efficacy of TAK-164 and the relationship to exposure, pharmacodynamic marker activation, and biodistribution was evaluated in xenograft models and primary human tumor xenograft (PHTX) models. We demonstrate TAK-164 selectively binds to, is internalized by, and has potent cytotoxic effects against GCC-expressing cells in vitro A single intravenous administration of TAK-164 (0.76 mg/kg) resulted in significant growth rate inhibition in PHTX models of metastatic colorectal cancer. Furthermore, imaging studies characterized TAK-164 uptake and activity and showed positive relationships between GCC expression and tumor uptake which correlated with antitumor activity. Collectively, our data suggest that TAK-164 is highly active in multiple GCC-positive tumors including those refractory to TAK-264, a GCC-targeted auristatin ADC. A strong relationship between uptake of 89Zr-labeled TAK-164, levels of GCC expression and, most notably, response to TAK-164 therapy in GCC-expressing xenografts and PHTX models. These data supported the clinical development of TAK-164 as part of a first-in-human clinical trial (NCT03449030).

A Translational Physiologically Based Pharmacokinetics/Pharmacodynamics Framework of Target-Mediated Disposition, Target Inhibition and Drug-Drug Interactions of Bortezomib.

Bortezomib is a potent 20S proteasome inhibitor approved for the treatment of multiple myeloma and mantle cell lymphoma. Despite the extensive clinical use of bortezomib, the mechanism of the complex time-dependent pharmacokinetics of bortezomib has not been fully investigated in context of its pharmacodynamics (PD) and drug-drug interaction (DDI) profiles. Here, we aimed to develop a mechanistic physiologically based (PB) PK/PD model to project PK, blood target inhibition and DDI of bortezomib in patients. A minimal PBPK/PD model consisting of six compartments was constructed using a bottom-up approach with pre-clinical data and human physiological parameters. Specifically, the target-mediated drug disposition (TMDD) of bortezomib in red blood cells (RBC), which determines target inhibition in blood, was characterized by incorporating the proteasome binding affinity of bortezomib and the proteasome concentration in RBC. The hepatic clearance and fraction metabolized by different CYP isoforms were estimated from in vitro metabolism and phenotyping experiments. The established model adequately characterized the multi-exponential and time-dependent plasma pharmacokinetics, target binding and blood proteasome inhibition of bortezomib. Further, the model was able to accurately predict the impact of a strong CYP3A inducer (rifampicin) and inhibitor (ketoconazole) on bortezomib exposure. In conclusion, the mechanistic PBPK/PD model successfully described the complex pharmacokinetics, target inhibition and DDIs of bortezomib in patients. This study illustrates the importance of incorporating target biology, drug-target interactions and in vitro clearance parameters into mechanistic PBPK/PD models and the utility of such models for pharmacokinetic, pharmacodynamic and DDI predictions.

A Minimal Physiologically-Based Pharmacokinetic Model Demonstrates Role of the Neonatal Fc Receptor (FcRn) Competition in Drug-Disease Interactions With Antibody Therapy.

Disease trajectories following antibody therapy can have a significant impact on the pharmacokinetics of the antibody. Although this phenomenon can often be explained by reduced target-expressing cells, other mechanisms may play a role. We use a novel minimal physiologically-based pharmacokinetic model to evaluate an alternative drug-disease interaction mechanism involving competitive inhibition of neonatal Fc receptor (FcRn)-mediated Immunoglobulin G recycling by paraproteins. The model is validated with clinical data from the anti-FcRn antibody M281 and is used to conduct a scenario test to quantify the interaction among M-protein, the characteristic paraprotein of multiple myeloma (MM), and the anti-CD38 antibody daratumumab indicated for MM treatment. Simulations predict up to a 3.6-fold increase in daratumumab half-life following M-protein reduction, which lends credence to the hypothesis that FcRn competition in MM can manifest as time-dependent reduction of clearance for daratumumab. This model can inform optimal dosing strategies for antibodies in MM and other pathologies of paraprotein excess.

A Citrulline-Based Translational Population System Toxicology Model for Gastrointestinal-Related Adverse Events Associated With Anticancer Treatments.

Gastrointestinal (GI)-related adverse events (AEs) are commonly observed in the clinic during cancer treatments. Citrulline is a potentially translatable biomarker of GI AEs. In this study, irinotecan-induced citrulline changes were studied for a range of doses and schedules in rats. A translational system toxicology model for GI AEs using citrulline was then developed based on new experimental data and parameters from a literature intestinal cell dynamic model. With the addition of feedback-development and tolerance-development mechanisms, the model well captured the plasma citrulline profiles after irinotecan treatment in rats. Subsequently, the model was translated to humans and predicted the observed GI AE dynamics in humans including dose-scheduling effect using the cytotoxic and feedback parameters estimated in rats with slight calibrations. This translational toxicology model could be used for other antineoplastic drugs to simulate various clinical dosing scenarios before human studies and mitigate potential GI AEs.

Overcoming Barriers to Hand Surgical Care in Low-Resource Settings.

There is a growing need for surgical treatment of hand injuries in low- and middle-income countries (LMICs). This rise in disease burden places more pressure on these health care systems that are already struggling to provide access to surgical care for their patients. Hand surgery outreach initiatives have increased in recent years and provide much needed care and relief to these countries. There are significant patient-, physician-, institution-, and infrastructure-related barriers associated with developing an outreach initiative. Understanding these barriers is essential in establishing a successful and meaningful outreach initiative.

C57BL/6 Substrain Differences in Pharmacological Effects after Acute and Repeated Nicotine Administration.

Tobacco smoking is the major cause of disability and death in the United States and around the world. In addition, tobacco dependence and addiction express themselves as complex behaviors involving an interplay of genetics, environment, and psychological state. Mouse genetic studies could potentially elucidate the novel genes and/or gene networks regulating various aspects of nicotine dependence. Using the closely related C57BL/6 (B6) mice substrains, recent reports have noted phenotypic differences within C57BL/6J (B6J) and C57BL/6N (B6N) mice for some drugs of abuse: alcohol, opiates, and cocaine. However, the differences in nicotine's effects have not yet been described in these substrains. We examined the phenotypic differences in these substrains following the acute and repeated administration of nicotine in several pharmacological measures, including locomotion (after acute and repeated exposure), body temperature, nociception, and anxiety-like behaviors. We report substrain differences in the pharmacological effects of acute and repeated nicotine administration in the B6 substrains. Overall, we show enhanced nicotine sensitivity to locomotion, hypothermia, antinociception, and anxiety-like behaviors in the B6J mouse substrain compared to B6N. In the repeated administration paradigm, both the B6N and B6J substrains showed no sensitized locomotor responses after repeated exposure to nicotine at the two doses tested. This study thus provides evidence that the B6 mouse substrains may be useful for genetic studies to elucidate some of the genetic variants involved in tobacco dependence and addiction.

Recommendations for the Design of Clinical Drug-Drug Interaction Studies With Itraconazole Using a Mechanistic Physiologically-Based Pharmacokinetic Model.

Regulatory agencies currently recommend itraconazole (ITZ) as a strong cytochrome P450 3A (CYP3A) inhibitor for clinical drug-drug interaction (DDI) studies. This work by an International Consortium for Innovation and Quality in Pharmaceutical Development working group (WG) is to develop and verify a mechanistic ITZ physiologically-based pharmacokinetic model and provide recommendations for optimal DDI study design based on model simulations. To support model development and verification, in vitro and clinical PK data for ITZ and its metabolites were collected from WG member companies. The model predictions of ITZ DDIs with seven different CYP3A substrates were within the guest criteria for 92% of area under the concentration-time curve ratios and 95% of maximum plasma concentration ratios, thus verifying the model for DDI predictions. The verified model was used to simulate various clinical DDI study scenarios considering formulation, duration of dosing, dose regimen, and food status to recommend the optimal design for maximal inhibitory effect by ITZ.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models.

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm3) divided by the time for control TV to reach 600mm3 and therefore not dependent on the chosen final time point tf. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

Comparison of uptake transporter functions in hepatocytes in different species to determine the optimal model for evaluating drug transporter activities in humans.

A thorough understanding of species-dependent differences in hepatic uptake transporters is critical for predicting human pharmacokinetics (PKs) from preclinical data. In this study, the activities of organic anion transporting polypeptide (OATP/Oatp), organic cation transporter 1 (OCT1/Oct1), and sodium-taurocholate cotransporting polypeptide (NTCP/Ntcp) in cultured rat, dog, monkey and human hepatocytes were compared. The activities of hepatic uptake transporters were evaluated with respect to culture duration, substrate and species-dependent differences in hepatocytes. Longer culture duration reduced hepatic uptake transporter activities across species except for Oatp and Ntcp in rats. Comparable apparent Michaelis-Menten constant (Km,app) values in hepatocytes were observed across species for atorvastatin, estradiol-17ß-glucuronide and metformin. The Km,app values for rosuvastatin and taurocholate were significantly different across species. Rat hepatocytes exhibited the highest Oatp percentage of uptake transporter-mediated permeation clearance (PSinf,act) while no difference in %PSinf,act of probe substrates were observed across species. The in vitro hepatocyte inhibition data in rats, monkeys and humans provided reasonable predictions of in vivo drug-drug interaction (DDIs) between atorvastatin/rosuvastatin and rifampin. These findings suggested that using human hepatocytes with a short culture time is the most robust preclinical model for predicting DDIs for compounds exhibiting active hepatic uptake in humans.

Application of unbound liver-to-plasma concentration ratio to quantitative projection of cytochrome P450-mediated drug-drug interactions using physiologically based pharmacokinetic modelling approach.

1. This study evaluated the prediction accuracy of cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) using minimal physiologically-based pharmacokinetic (PBPK) modelling incorporating the hepatic accumulation factor of an inhibitor (i.e. unbound liver/unbound plasma concentration ratio [Kp,uu,liver]) based on 22 clinical DDI studies. 2. Kp,uu,liver values were estimated using three methods: (1) ratio of cell-to-medium ratio in human cryopreserved hepatocytes (C/Mu) at 37 °C to that on ice (Kp,uu,C/M), (2) multiplication of total liver/unbound plasma concentration ratio (Kp,u,liver) estimated from C/Mu at 37 °C with unbound fraction in human liver homogenate (Kp,uu,cell) and (3) observed Kp,uu,liver in rats after intravenous infusion (Kp,uu,rat). 3. PBPK model using each Kp,uu,liver projected the area under the curve (AUC) increase of substrates more accurately than the model assuming a Kp,uu,liver of 1 for the average fold error and root mean square error did. Particularly, the model with a Kp,uu,liver of 1 underestimated the AUC increase of triazolam following co-administration with CYP3A4 inhibitor itraconazole by five-fold, whereas the AUC increase projected using the model incorporating the Kp,uu,C/M, Kp,uu,cell, or Kp,uu,rat of itraconazole and hydroxyitraconazole was within approximately two-fold of the actual value. 4. The results indicated that incorporating Kp,uu,liver into the PBPK model improved the accuracy of DDI projection.

The Role of Triangular Fibrocartilage Complex in Axial Stability of the Forearm.

Background The triangular fibrocartilage complex (TFCC) provides stability to the wrist and disruption of this complex can result in axial instability which can lead to ulnocarpal abutment. Purpose This article determines the individual contributions of the volar radioulnar ligament (VRUL), dorsal radioulnar ligament (DRUL), and foveal attachment of the TFCC to longitudinal stability of the forearm under axial load. Materials and Methods Eighteen cadaveric specimens were randomly assigned into three groups representing the component of the TFCC to be initially transected: VRUL, DRUL, and foveal attachment. Prior to transection, posterioranterior radiographs of the wrist were obtained at 0, 44.5, and 90 N of axial load. Serial transection of the TFCC components were performed with radiographs obtained at each of the aforementioned loads. Ulnar variance was assessed with two-way repeated measures analysis of variance and paired t -tests. Results Transection of the foveal attachment demonstrated a significant change in ulnar variance of 1.5 and 0.6 mm under 45 and 90 N of load, respectively. At 0 N of load, there was no significant change in ulnar variance between an intact wrist and a wrist with all three ligaments transected; however, a significant change in ulnar variance, 1.0 mm, was observed under 90 N of load. Conclusion The foveal attachment of the TFCC provides the largest contribution to axial stability. Clinical Relevance The TFCC provides axial stability to the wrist and disruption of the TFCC may result in change in ulnar variance observed on an axial loaded wrist radiograph.

The Effect of Axial Loading on Ulnar Variance.

Background Forearm rotation results in change in ulnar variance. Axial loading of the wrist is required to maintain daily activities. Change in ulnar variance during axial loading has not been investigated previously. Purpose To measure the change in ulnar variance on axially loaded wrists. Patients and Methods We examined 21 asymptomatic individuals and 24 patients with unilateral ulnar-sided wrist pain. All patients underwent standard neutral posteroanterior wrist radiographs without load and under axial loading on bilateral wrists. Axial loading was standardized at 18.1 kgf using an analog weight scale. A magnetic resonance (MR) arthrogram was obtained only in patients with ulnar-sided wrist pain. Beighton flexibility score was recorded on healthy volunteers. Change in ulnar variance between 0 and 18.1 kgf was compared for each wrist among all subjects. A correlation was sought between the change in ulnar variance, MR arthrogram findings, and physical examination. Results In individuals without wrist pain, on average, 0.4 mm increase in ulnar variance was measured between 0 and 18.1 kgf. There was no difference between the dominant and nondominant side. No correlation was found with increasing age. In contrast, patients with ulnar-sided wrist pain displayed an average increase of 0.8 mm in ulnar variance. Compared with the contralateral wrist, more than 1 mm increase in ulnar variance was correlated with intra-articular pathologies including dorsoulnar ligament disruption, central triangular fibrocartilage complex (TFCC) perforation, and foveal detachment. Conclusion Compared with contralateral side, more than 1 mm increase in ulnar variance is suggestive of longitudinal instability or TFCC pathology. Level of Evidence Level II, diagnostic.