Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma.

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n = 19, discovery dataset) treated with ICIs, despite reducing dimensionality by >100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response, and were characterized by differentially active and cell state-specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n = 209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (P < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative costimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.

PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.

Implications of topological imbalance for representation learning on biomedical knowledge graphs.

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.

Measurements of protein-ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein-ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4-0.6 log units and when ideal probability estimates between 0.4-0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold.

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as ß-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints.

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.

New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets.

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.

Discovery of retinoic acid receptor agonists as proliferators of cardiac progenitor cells through a phenotypic screening approach.

Identification of small molecules with the potential to selectively proliferate cardiac progenitor cells (CPCs) will aid our understanding of the signaling pathways and mechanisms involved and could ultimately provide tools for regenerative therapies for the treatment of post-MI cardiac dysfunction. We have used an in vitro human induced pluripotent stem cell-derived CPC model to screen a 10,000-compound library containing molecules representing different target classes and compounds reported to modulate the phenotype of stem or primary cells. The primary readout of this phenotypic screen was proliferation as measured by nuclear count. We identified retinoic acid receptor (RAR) agonists as potent proliferators of CPCs. The CPCs retained their progenitor phenotype following proliferation and the identified RAR agonists did not proliferate human cardiac fibroblasts, the major cell type in the heart. In addition, the RAR agonists were able to proliferate an independent source of CPCs, HuES6. The RAR agonists had a time-of-differentiation-dependent effect on the HuES6-derived CPCs. At 4 days of differentiation, treatment with retinoic acid induced differentiation of the CPCs to atrial cells. However, after 5 days of differentiation treatment with RAR agonists led to an inhibition of terminal differentiation to cardiomyocytes and enhanced the proliferation of the cells. RAR agonists, at least transiently, enhance the proliferation of human CPCs, at the expense of terminal cardiac differentiation. How this mechanism translates in vivo to activate endogenous CPCs and whether enhancing proliferation of these rare progenitor cells is sufficient to enhance cardiac repair remains to be investigated.

Drug Screening in Human PSC-Cardiac Organoids Identifies Pro-proliferative Compounds Acting via the Mevalonate Pathway.

We have previously developed a high-throughput bioengineered human cardiac organoid (hCO) platform, which provides functional contractile tissue with biological properties similar to native heart tissue, including mature, cell-cycle-arrested cardiomyocytes. In this study, we perform functional screening of 105 small molecules with pro-regenerative potential. Our findings reveal surprising discordance between our hCO system and traditional 2D assays. In addition, functional analyses uncovered detrimental effects of many hit compounds. Two pro-proliferative small molecules without detrimental impacts on cardiac function were identified. High-throughput proteomics in hCO revealed synergistic activation of the mevalonate pathway and a cell-cycle network by the pro-proliferative compounds. Cell-cycle reentry in hCO and in vivo required the mevalonate pathway as inhibition of the mevalonate pathway with a statin attenuated pro-proliferative effects. This study highlights the utility of human cardiac organoids for pro-regenerative drug development, including identification of underlying biological mechanisms and minimization of adverse side effects.

High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets.

Over 5 million people in the United States suffer from heart failure, due to the limited ability to regenerate functional cardiac tissue. One potential therapeutic strategy is to enhance proliferation of resident cardiomyocytes. However, phenotypic screening for therapeutic agents is challenged by the limited ability of conventional markers to discriminate between cardiomyocyte proliferation and endoreplication (e.g. polyploidy and multinucleation). Here, we developed a novel assay that combines automated live-cell microscopy and image processing algorithms to discriminate between proliferation and endoreplication by quantifying changes in the number of nuclei, changes in the number of cells, binucleation, and nuclear DNA content. We applied this assay to further prioritize hits from a primary screen for DNA synthesis, identifying 30 compounds that enhance proliferation of human induced pluripotent stem cell-derived cardiomyocytes. Among the most active compounds from the phenotypic screen are clinically approved L-type calcium channel blockers from multiple chemical classes whose activities were confirmed across different sources of human induced pluripotent stem cell-derived cardiomyocytes. Identification of compounds that stimulate human cardiomyocyte proliferation may provide new therapeutic strategies for heart failure.

Safety and Efficacy of Intraoperative Computer-Navigated Versus Non-Navigated Shoulder Arthroplasty at a Tertiary Referral.

Emerging technologies in shoulder arthroplasty, such as 3-dimensional planning software and real-time intraoperative navigation, are now available for surgeons to perform more accurate placement of the glenoid component without malposition or perforation. Using these tools, the surgeon can visualize the version, inclination, and containment of the implant and determine whether augmented components would be necessary. This review provides an updated investigation of the present literature to elucidate the role of computer navigation in modern shoulder arthroplasty.

Cultivating Innovative Pragmatic Cluster-Randomized Registry Trials Embedded in Hemodialysis Care: Workshop Proceedings From 2018.

Hemodialysis is a life-sustaining treatment for persons with kidney failure. However, those on hemodialysis still face a poor quality of life and a short life expectancy. High-quality research evidence from large randomized controlled trials is needed to identify interventions that improve the experiences, outcomes, and health care of persons receiving hemodialysis. With the support of the Canadian Institutes of Health Research and its Strategy for Patient-Oriented Research, the Innovative Clinical Trials in Hemodialysis Centers initiative brought together Canadian and international kidney researchers, patients, health care providers, and health administrators to participate in a workshop held in Toronto, Canada, on June 2 and 3, 2018. The workshop served to increase knowledge and awareness about the conduct of innovative, pragmatic, cluster-randomized registry trials embedded into routine hemodialysis care and provided an opportunity to discuss and build support for new trial ideas. The workshop content included structured presentations, facilitated group discussions, and expert panel feedback. Partnerships and promising trial ideas borne out of the workshop will continue to be developed to support the implementation of future large-scale trials.

L'hémodialyse constitue un traitement essentiel au maintien de la vie pour les personnes atteintes d'insuffisance rénale. Les patients hémodialysés voient cependant leur qualité et leur espérance de vie réduites. Des données de recherches probantes, provenant de vastes essais cliniques contrôlés à répartition aléatoire, sont nécessaires pour améliorer l'expérience, les résultats et les soins des patients hémodialysés. Grâce au soutien des Instituts de recherche en santé du Canada (IRSC) et de leur Stratégie de recherche axée sur le patient (SRAP), l'initiative sur les essais cliniques novateurs (ECN) en centres d'hémodialyse a réuni divers intervenants en santé rénale (chercheurs, patients, fournisseurs de soins et administrateurs), du Canada et de partout dans le monde, lors d'un colloque qui s'est tenu à Toronto les 2 et 3 juin 2018. Ce colloque a permis d'accroître la sensibilisation et les connaissances sur la conduite d'essais cliniques novateurs, répartis en grappes, pragmatiques et intégrés aux soins d'hémodialyse de routine. Cette rencontre a également fourni une occasion de discuter de nouvelles idées d'essais cliniques et de susciter les appuis nécessaires à leur réalisation. Le colloque s'est déroulé sous forme de présentations structurées, de discussions animées en groupe et de rétroaction de la part d'un comité d'experts. Les idées de recherche prometteuses et les partenariats issus de ce colloque continueront d'être développés pour soutenir la réalisation d'essais cliniques futurs de grande envergure.

Validation of Neck-Shaft Angle Correction After Cephalomedullary Nail Fixation.

OBJECTIVES: To validate a neck-shaft angle (NSA) correction formula using the known angle between the medullary nail and lag screw of an indwelling cephalomedullary nail (CMN). METHODS: Pertrochanteric femur fractures were created in 7 synthetic femurs and reconstructed with a CMN. Anteroposterior x-rays were taken with the proximal femur in 0, 15, 30, and 45 degrees of internal/external rotation and 10 and 20 degrees of flexion with neutral rotation. The femoral NSA and the nail angle were measured on anteroposterior x-rays. The corrected NSA was obtained by multiplying the known nail angle by the quotient of the measured NSA over the measured nail angle. RESULTS: With increasing degrees of internal or external rotation, the difference between the actual and the measured NSA increased. After correction using the known angle of the nail, the measured NSA was reduced to within 3 degrees of the actual NSA over all degrees of rotation. At neutral rotation, 15 degrees of external rotation, and 10 and 20 degrees of hip flexion, there was minimal difference between the actual NSA and the measured NSA, negating the effect of the correction formula. CONCLUSIONS: The measured NSA after CMN of intertrochanteric fractures is altered by hip rotation. This study validates the correction formula, which determines this difference by using the known angle between the lag screw and the nail.

The brain-penetrant clinical ATM inhibitor AZD1390 radiosensitizes and improves survival of preclinical brain tumor models.

Poor survival rates of patients with tumors arising from or disseminating into the brain are attributed to an inability to excise all tumor tissue (if operable), a lack of blood-brain barrier (BBB) penetration of chemotherapies/targeted agents, and an intrinsic tumor radio-/chemo-resistance. Ataxia-telangiectasia mutated (ATM) protein orchestrates the cellular DNA damage response (DDR) to cytotoxic DNA double-strand breaks induced by ionizing radiation (IR). ATM genetic ablation or pharmacological inhibition results in tumor cell hypersensitivity to IR. We report the primary pharmacology of the clinical-grade, exquisitely potent (cell IC50, 0.78 nM), highly selective [>10,000-fold over kinases within the same phosphatidylinositol 3-kinase-related kinase (PIKK) family], orally bioavailable ATM inhibitor AZD1390 specifically optimized for BBB penetration confirmed in cynomolgus monkey brain positron emission tomography (PET) imaging of microdosed 11C-labeled AZD1390 (Kp,uu, 0.33). AZD1390 blocks ATM-dependent DDR pathway activity and combines with radiation to induce G2 cell cycle phase accumulation, micronuclei, and apoptosis. AZD1390 radiosensitizes glioma and lung cancer cell lines, with p53 mutant glioma cells generally being more radiosensitized than wild type. In in vivo syngeneic and patient-derived glioma as well as orthotopic lung-brain metastatic models, AZD1390 dosed in combination with daily fractions of IR (whole-brain or stereotactic radiotherapy) significantly induced tumor regressions and increased animal survival compared to IR treatment alone. We established a pharmacokinetic-pharmacodynamic-efficacy relationship by correlating free brain concentrations, tumor phospho-ATM/phospho-Rad50 inhibition, apoptotic biomarker (cleaved caspase-3) induction, tumor regression, and survival. On the basis of the data presented here, AZD1390 is now in early clinical development for use as a radiosensitizer in central nervous system malignancies.

Orally Bioavailable and Blood-Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice.

Inhibition of ataxia-telangiectasia mutated (ATM) during radiotherapy of glioblastoma multiforme (GBM) may improve tumor control by short-circuiting the response to radiation-induced DNA damage. A major impediment for clinical implementation is that current inhibitors have limited central nervous system (CNS) bioavailability; thus, the goal was to identify ATM inhibitors (ATMi) with improved CNS penetration. Drug screens and refinement of lead compounds identified AZ31 and AZ32. The compounds were then tested in vivo for efficacy and impact on tumor and healthy brain. Both AZ31 and AZ32 blocked the DNA damage response and radiosensitized GBM cells in vitro AZ32, with enhanced blood-brain barrier (BBB) penetration, was highly efficient in vivo as radiosensitizer in syngeneic and human, orthotopic mouse glioma model compared with AZ31. Furthermore, human glioma cell lines expressing mutant p53 or having checkpoint-defective mutations were particularly sensitive to ATMi radiosensitization. The mechanism for this p53 effect involves a propensity to undergo mitotic catastrophe relative to cells with wild-type p53. In vivo, apoptosis was >6-fold higher in tumor relative to healthy brain after exposure to AZ32 and low-dose radiation. AZ32 is the first ATMi with oral bioavailability shown to radiosensitize glioma and improve survival in orthotopic mouse models. These findings support the development of a clinical-grade, BBB-penetrating ATMi for the treatment of GBM. Importantly, because many GBMs have defective p53 signaling, the use of an ATMi concurrent with standard radiotherapy is expected to be cancer-specific, increase the therapeutic ratio, and maintain full therapeutic effect at lower radiation doses. Mol Cancer Ther; 17(8); 1637-47. ©2018 AACR.

Short- to mid-term outcomes of anatomic MCL reconstruction with Achilles tendon allograft after multiligament knee injury.

PURPOSE: Multiple techniques have been described in the literature for reconstruction of the medial collateral ligament. The purpose of this study is to describe functional outcome, range of motion, and knee stability following anatomic MCL reconstruction utilizing an Achilles tendon bone allograft after multiligament knee injury. METHODS: A comprehensive search of a single-hospital multiligament knee injury (MLKI) procedural database was conducted to identify all patients that underwent reconstruction of the MCL utilizing an Achilles tendon bone allograft and with 2-year clinical follow-up. Medical charts were retrospectively reviewed to determine each patient's knee dislocation (KD) grade, final range of motion, stability on clinical examination, and the incidence of complications and reoperations. KOOS, IKDC, and Marx scores were also collected. RESULTS: Thirty-two knees in 32 patients (21 males and 11 females) with a mean age of 30 years (range 15-51) were followed for an average of 40 months (range 28-87 months) following MCL reconstruction with Achilles tendon bone allograft. For patients with multiligament knee injuries, there were 14 KD-I (11 ACL/MCL; 3 MCL/PCL; 1 MCL/ACL/LCL; 1 MCL/PCL/LCL), 12 KD 3-M, and 3 KD-IV. One patient underwent isolated revision MCL reconstruction. At final follow-up, clinically significant valgus laxity was observed in only 1 patient (3%). All patients were able to achieve full extension of the knee and the average flexion was 121.1 ± 19.6. The average IKDC score was 67.6 ± 19.9 (range 27.7-98.9), the average KOOS score 77.1 ± 16.8 (range 31-100). The average Marx score was 4.9 (range 0-16, SD 5.2). Thirty-one of 32 (96%) patients reported being satisfied with results of the surgery. Knee dislocation grades were significantly correlated with post-operative outcome measures. CONCLUSION: In a series utilizing a modified Marx Achilles tendon, MCL reconstruction in the setting of MLKI demonstrated satisfactory clinical and functional outcomes, as well as patient satisfaction at short- to mid-term follow-up. Furthermore, knee dislocation grades were demonstrated to correlate with post-operative IKDC, KOOS, and Marx scores. LEVEL OF EVIDENCE: Type IV.

Does the Angle of the Nail Matter for Pertrochanteric Fracture Reduction? Matching Nail Angle and Native Neck-Shaft Angle.

OBJECTIVES: To determine whether fixation of pertrochanteric hip fractures with cephalomedullary nails (CMNs) with a neck-shaft angle (NSA) less than the native NSA affects reduction and lag screw cutout. DESIGN: Retrospective comparative study. SETTING: Level I trauma center. PATIENTS/PARTICIPANTS: Patients treated with a CMN for unstable pertrochanteric femur fractures (OTA/AO 31-A2.2 and 31-A2.3) between 2005 and 2014. INTERVENTION: CMN fixation. MAIN OUTCOME MEASUREMENT: NSA reduction and lag screw cutout. RESULTS: Patients fixed with a nail angle less than their native NSA were less likely to have good reductions [17% vs. 60%, 95% confidence interval (CI), -63% to -18%; P = 0.0005], secondary to more varus reductions (41% vs. 10%, 95% CI, 9%-46%; P = 0.01) and more fractures with ≥4 mm of displacement (63% vs. 35%, 95% CI, 3%-49%; P = 0.03). The cutout was not associated with the use of a nail angle less than the native NSA (60% vs. 76%, 95% CI, -56% to 18%; P = 0.5), varus reductions (60% vs. 32%, 95% CI, -13% to 62%; P = 0.3), or poor reductions (20% vs. 17%, 95% CI, -24% to 44%; P = 1.0). CONCLUSIONS: The fixation of unstable pertrochanteric hip fractures with a nail angle less than the native NSA was associated with more varus reductions and fracture displacement but did not affect the lag screw cutout. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.