Assessing the Accuracy and Efficiency of Free Energy Differences Obtained from Reweighted Flow-Based Probabilistic Generative Models.

Computing free energy differences between metastable states characterized by nonoverlapping Boltzmann distributions is often a computationally intensive endeavor, usually requiring chains of intermediate states to connect them. Targeted free energy perturbation (TFEP) can significantly lower the computational cost of FEP calculations by choosing a set of invertible maps used to directly connect the distributions of interest, achieving the necessary statistically significant overlaps without sampling any intermediate states. Probabilistic generative models (PGMs) based on normalizing flow architectures can make it much easier via machine learning to train invertible maps needed for TFEP. However, the accuracy and applicability of approaches based on empirically learned maps depend crucially on the choice of reweighting method adopted to estimate the free energy differences. In this work, we assess the accuracy, rate of convergence, and data efficiency of different free energy estimators, including exponential averaging, Bennett acceptance ratio (BAR), and multistate Bennett acceptance ratio (MBAR), in reweighting PGMs trained by maximum likelihood on limited amounts of molecular dynamics data sampled only from end-states of interest. We carry out the comparisons on a set of simple but representative case studies, including conformational ensembles of alanine dipeptide and ibuprofen. Our results indicate that BAR and MBAR are both data efficient and robust, even in the presence of significant model overfitting in the generation of invertible maps. This analysis can serve as a stepping stone for the deployment of efficient and quantitatively accurate ML-based free energy calculation methods in complex systems.

Influence of Solvent Selection on the Crystallizability and Polymorphic Selectivity Associated with the Formation of the "Disappeared" Form I Polymorph of Ritonavir.

The comparative crystallizability and polymorphic selectivity of ritonavir, a novel protease inhibitor for the treatment of acquired immune-deficiency syndrome, as a function of solvent selection are examined through an integrated and self-consistent experimental and computational molecular modeling study. Recrystallization at high supersaturation by rapid cooling at 283.15 K is found to produce the metastable "disappeared" polymorphic form I from acetone, ethyl acetate, acetonitrile, and toluene solutions in contrast to ethanol which produces the stable form II. Concomitant crystallization of the other known solid forms is not found under these conditions. Isothermal crystallization studies using turbidometric detection based upon classical nucleation theory reveal that, for an equal induction time, the required driving force needed to initiate solution nucleation decreases with solubility in the order of ethanol, acetone, acetonitrile, ethyl acetate, and toluene consistent with the expected desolvation behavior predicted from the calculated solute solvation free energies. Molecular dynamics simulations of the molecular and intermolecular chemistry reveal the presence of conformational interplay between intramolecular and intermolecular interactions within the solution phase. These encompass the solvent-dependent formation of intramolecular O-H...O hydrogen bonding between the hydroxyl and carbamate groups coupled with differing conformations of the hydroxyl's shielding phenyl groups. These conformational preferences and their relative interaction propensities, as a function of solvent selection, may play a rate-limiting role in the crystallization behavior by not only inhibiting to different degrees the nucleation process but also restricting the assembly of the optimal intermolecular hydrogen bonding network needed for the formation of the stable form II polymorph.

Efficient Determination of Critical Water Activity and Classification of Hydrate-Anhydrate Stability Relationship.

For a pair of hydrated and anhydrous crystals, the hydrate is more stable than the anhydrate when the water activity is above the critical water activity (awc). Conventional methods to determine awc are based on either hydrate-anhydrate competitive slurries at different aw or solubilities measured at different temperatures. However, these methods are typically resource-intensive and time-consuming. Here, we present simple and complementary solution- and solid-based methods and illustrate them using carbamazepine and theophylline. In the solution-based method, awc can be predicted using intrinsic dissolution rate (IDR) ratio or solubility ratio of the hydrate-anhydrate pair measured at a known water activity. In the solid-based method, awc is predicted as a function of temperature from the dehydration temperature and enthalpy obtained by differential scanning calorimetry (DSC) near a water activity of unity. For carbamazepine and theophylline, the methods yielded awc values in good agreement with those from the conventional methods. By incorporating awc as an additional variable, the hydrate-anhydrate relationship is categorized into four classes based on their dehydration temperature (Td) and enthalpy (ΔHd) in analogy with the monotropy/enantiotropy classification for crystal polymorphs. In Class 1 (ΔHd< 0 and Td ≥ 373 K), no awc exists. In Class 2 (ΔHd>0andTd≥373K), awc always exists under conventional crystallization conditions. In Class 3 (ΔHd<0andTd<373K), awc exists when T>Td. In Class 4 (ΔHd>0andTd<373K), awc exists only when T

Predicting crystal form stability under real-world conditions.

The physicochemical properties of molecular crystals, such as solubility, stability, compactability, melting behaviour and bioavailability, depend on their crystal form1. In silico crystal form selection has recently come much closer to realization because of the development of accurate and affordable free-energy calculations2-4. Here we redefine the state of the art, primarily by improving the accuracy of free-energy calculations, constructing a reliable experimental benchmark for solid-solid free-energy differences, quantifying statistical errors for the computed free energies and placing both hydrate crystal structures of different stoichiometries and anhydrate crystal structures on the same energy landscape, with defined error bars, as a function of temperature and relative humidity. The calculated free energies have standard errors of 1-2 kJ mol-1 for industrially relevant compounds, and the method to place crystal structures with different hydrate stoichiometries on the same energy landscape can be extended to other multi-component systems, including solvates. These contributions reduce the gap between the needs of the experimentalist and the capabilities of modern computational tools, transforming crystal structure prediction into a more reliable and actionable procedure that can be used in combination with experimental evidence to direct crystal form selection and establish control5.

Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions.

Early assessment of crystalline thermodynamic solubility continues to be elusive for drug discovery and development despite its critical importance, especially for the ever-increasing fraction of poorly soluble drug candidates. Here we present a detailed evaluation of a physics-based free energy perturbation (FEP+) approach for computing the thermodynamic aqueous solubility. The predictive power of this approach is assessed across diverse chemical spaces, spanning pharmaceutically relevant literature compounds and more complex AbbVie compounds. Our approach achieves predictive (RMSE = 0.86) and differentiating power (R2 = 0.69) and therefore provides notably improved correlations to experimental solubility compared to state-of-the-art machine learning approaches that utilize quantum mechanics-based descriptors. The importance of explicit considerations of crystalline packing in predicting solubility by the FEP+ approach is also highlighted in this study. Finally, we show how computed energetics, including hydration and sublimation free energies, can provide further insights into molecule design to feed the medicinal chemistry DMTA cycle.

Pharmaceutical Development Challenges in a Beyond Rule of Five Prodrug: Case Study of ABBV-167, Phosphate Prodrug of Venetoclax.

ABBV-167, a phosphate prodrug of BCL-2 inhibitor venetoclax, was recently progressed into the clinic as an alternative means of reducing pill burden for patients in high-dose indications. The dramatically enhanced aqueous solubility of ABBV-167 allowed for high drug loading within a crystalline tablet and, when administered in phase I clinical study, conferred venetoclax exposure commensurate with the equivalent dose administered as an amorphous solid dispersion. In enabling the progression into the clinic, we performed a comprehensive evaluation of the CMC development aspects of this beyond the rule of five (bRo5) prodrug. Adding a phosphate moiety resulted in excessively complex chemical speciation and solid form landscapes with significant physical-chemical stability liabilities. A combination of experimental and computational methods including microelectron diffraction (MicroED), total scattering, tablet colorimetry, finite element, and molecular dynamics modeling were used to understand CMC developability across drug substance and product manufacture and storage. The prodrug's chemical structural characteristics and loose crystal packing were found to be responsible for the loss of crystallinity during its manufacturing, which in turn led to high solid-state chemical reactivity and poor shelf life stability. The ABBV-167 case exemplifies key CMC development challenges for complex chemical matter such as bRo5 phosphate prodrugs with significant ramifications during drug substance and drug product manufacturing and storage.

Association of Thoracic Aortic Aneurysm Size With Long-term Patient Outcomes: The KP-TAA Study.

Importance: The risk of adverse events from ascending thoracic aorta aneurysm (TAA) is poorly understood but drives clinical decision-making. Objective: To evaluate the association of TAA size with outcomes in nonsyndromic patients in a large non-referral-based health care delivery system. Design, Setting, and Participants: The Kaiser Permanente Thoracic Aortic Aneurysm (KP-TAA) cohort study was a retrospective cohort study at Kaiser Permanente Northern California, a fully integrated health care delivery system insuring and providing care for more than 4.5 million persons. Nonsyndromic patients from a regional TAA safety net tracking system were included. Imaging data including maximum TAA size were merged with electronic health record (EHR) and comprehensive death data to obtain demographic characteristics, comorbidities, medications, laboratory values, vital signs, and subsequent outcomes. Unadjusted rates were calculated and the association of TAA size with outcomes was evaluated in multivariable competing risk models that categorized TAA size as a baseline and time-updated variable and accounted for potential confounders. Data were analyzed from January 2018 to August 2021. Exposures: TAA size. Main Outcomes and Measures: Aortic dissection (AD), all-cause death, and elective aortic surgery. Results: Of 6372 patients with TAA identified between 2000 and 2016 (mean [SD] age, 68.6 [13.0] years; 2050 female individuals [32.2%] and 4322 male individuals [67.8%]), mean (SD) initial TAA size was 4.4 (0.5) cm (828 individuals [13.0% of cohort] had initial TAA size 5.0 cm or larger and 280 [4.4%] 5.5 cm or larger). Rates of AD were low across a mean (SD) 3.7 (2.5) years of follow-up (44 individuals [0.7% of cohort]; incidence 0.22 events per 100 person-years). Larger initial aortic size was associated with higher risk of AD and all-cause death in multivariable models, with an inflection point in risk at 6.0 cm. Estimated adjusted risks of AD within 5 years were 0.3% (95% CI, 0.3-0.7), 0.6% (95% CI, 0.4-1.3), 1.5% (95% CI, 1.2-3.9), 3.6% (95% CI, 1.8-12.8), and 10.5% (95% CI, 2.7-44.3) in patients with TAA size of 4.0 to 4.4 cm, 4.5 to 4.9 cm, 5.0 to 5.4 cm, 5.5 to 5.9 cm, and 6.0 cm or larger, respectively, in time-updated models. Rates of the composite outcome of AD and all-cause death were higher than for AD alone, but a similar inflection point for increased risk was observed at 6.0 cm. Conclusions and Relevance: In a large sociodemographically diverse cohort of patients with TAA, absolute risk of aortic dissection was low but increased with larger aortic sizes after adjustment for potential confounders and competing risks. Our data support current consensus guidelines recommending prophylactic surgery in nonsyndromic individuals with TAA at a 5.5-cm threshold.

A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates.

Predictions of the structures of stoichiometric, fractional, or nonstoichiometric hydrates of organic molecular crystals are immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for nonstoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP) protocol. Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structure. The method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and nonstoichiometric hydrate structures. This approach, which we term a mapping approach for crystal hydrates (MACH), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that our mapping approach introduces an advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.

Efficient Crystal Structure Prediction for Structurally Related Molecules with Accurate and Transferable Tailor-Made Force Fields.

Crystal structure prediction (CSP) is generally used to complement experimental solid form screening and applied to individual molecules in drug development. The fast development of algorithms and computing resources offers the opportunity to use CSP earlier and for a broader range of applications in the drug design cycle. This study presents a novel paradigm of CSP specifically designed for structurally related molecules, referred to as Quick-CSP. The approach prioritizes more accurate physics through robust and transferable tailor-made force fields (TMFFs), such that significant efficiency gains are achieved through the reduction of expensive ab initio calculations. The accuracy of the TMFF is increased by the introduction of electrostatic multipoles, and the fragment-based force field parameterization scheme is demonstrated to be transferable for a family of chemically related molecules. The protocol is benchmarked with structurally related compounds from the Bromodomain and Extraterminal (BET) domain inhibitors series. A new convergence criterion is introduced that aims at performing only as many ab initio optimizations of crystal structures as required to locate the bottom of the crystal energy landscape within a user-defined accuracy. The overall approach provides significant cost savings ranging from three- to eight-fold less than the full-CSP workflow. The reported advancements expand the scope and utility of the underlying CSP building blocks as well as their novel reassembly to other applications earlier in the drug design cycle to guide molecule design and selection.

Overcoming Bioavailability Challenges of Dasabuvir and Enabling a Triple-Combination Direct-Acting Antiviral HCV Regimen through a Salt of Very Weak Acid for Oral Delivery.

Dasabuvir is a non-nucleoside polymerase inhibitor for the treatment of hepatitis C virus (HCV) infection. It is an extremely weak diacidic drug (pKa = 8.2 and 9.2) and a prolific solvate former. Due to its exceedingly low aqueous solubility (≤0.127 µg/mL at pH 1-6.8, dose number of 1.31 × 104), crystalline dasabuvir free acid exhibited poor oral bioavailability in initial animal pharmacokinetic (PK) assessment. This necessitated the development of enabling formulation for human clinical studies to achieve the required therapeutic in vivo concentration of dasabuvir. While salt formation has been widely used to enhance the solubility and dissolution rate of solids, this approach has rarely been applied to develop oral solid dosage forms for acidic drugs as weak as dasabuvir due to concerns of rapid disproportionation and crystallization of its free acid. In this contribution, we detail our efforts in identifying dasabuvir monosodium monohydrate as a drug substance that is stable, manufacturable, and, most importantly, significantly enhances the dissolution and oral absorption of this poorly soluble drug. The oral delivery of dasabuvir through the salt approach has enabled the commercialization of the triple-combination direct-acting antiviral HCV regimen, Viekira Pak. The methodologies and solutions identified in targeted studies to overcome technical challenges encountered along the way (i.e., incorporation of polymers to inhibit crystallization and disproportionation and species mapping to enable salt manufacturing process, etc.) can be applied to other insoluble compounds.

Understanding stress-induced disorder and breakage in organic crystals: beyond crystal structure anisotropy.

Crystal engineering has advanced the strategies for design and synthesis of organic solids with the main focus being on customising the properties of the materials. Research in this area has a significant impact on large-scale manufacturing, as industrial processes may lead to the deterioration of such properties due to stress-induced transformations and breakage. In this work, we investigate the mechanical properties of structurally related labile multicomponent solids of carbamazepine (CBZ), namely the dihydrate (CBZ·2H2O), a cocrystal of CBZ with 1,4-benzoquinone (2CBZ·BZQ) and the solvates with formamide and 1,4-dioxane (CBZ·FORM and 2CBZ·DIOX, respectively). The effect of factors that are external (e.g. impact stressing) and/or internal (e.g. phase transformations and thermal motion) to the crystals are evaluated. In comparison to the other CBZ multicomponent crystal forms, CBZ·2H2O crystals tolerate less stress and are more susceptible to breakage. It is shown that this poor resistance to fracture may be a consequence of the packing of CBZ molecules and the orientation of the principal molecular axes in the structure relative to the cleavage plane. It is concluded, however, that the CBZ lattice alone is not accountable for the formation of cracks in the crystals of CBZ·2H2O. The strength and the temperature-dependence of electrostatic interactions, such as hydrogen bonds between CBZ and coformer, appear to influence the levels of stress to which the crystals are subjected that lead to fracture. Our findings show that the appropriate selection of coformer in multicomponent crystal forms, targetting superior mechanical properties, needs to account for the intrinsic stress generated by molecular vibrations and not solely by crystal anisotropy. Structural defects within the crystal lattice, although highly influenced by the crystallisation conditions and which are especially difficult to control in organic solids, may also affect breakage.

Implications of the Conformationally Flexible, Macrocyclic Structure of the First-Generation, Direct-Acting Anti-Viral Paritaprevir on Its Solid Form Complexity and Chameleonic Behavior.

Direct-acting antiviral regimens have transformed therapeutic management of hepatitis C across all prevalent genotypes. Most of the chemical matter in these regimens comprises molecules well outside the traditional drug development chemical space and presents significant challenges. Herein, the implications of high conformational flexibility and the presence of a 15-membered macrocyclic ring in paritaprevir are studied through a combination of advanced computational and experimental methods with focus on molecular chameleonicity and crystal form complexity. The ability of the molecule to toggle between high and low 3D polar surface area (PSA) conformations is underpinned by intramolecular hydrogen bonding (IMHB) interactions and intramolecular steric effects. Computational studies consequently show a very significant difference of over 75 Å2 in 3D PSA between polar and apolar environments and provide the structural basis for the perplexingly favorable passive permeability of the molecule. Crystal packing and protein binding resulting in strong intermolecular interactions disrupt these intramolecular interactions. Crystalline Form I benefits from strong intermolecular interactions, whereas the weaker intermolecular interactions in Form II are partially compensated by the energetic advantage of an IMHB. Like Form I, no IMHB is observed within the receptor-bound conformation; instead, an intermolecular H-bond contributes to the potency of the molecule. The choice of metastable Form II is derisked through strategies accounting for crystal surface and packing features to manage higher form specific solid-state chemical reactivity and specific processing requirements. Overall, the results show an unambiguous link between structural features and derived properties from crystallization to dissolution, permeation, and docking into the protein pocket.

Molecular, Solid-State and Surface Structures of the Conformational Polymorphic Forms of Ritonavir in Relation to their Physicochemical Properties.

PURPOSE: Application of multi-scale modelling workflows to characterise polymorphism in ritonavir with regard to its stability, bioavailability and processing. METHODS: Molecular conformation, polarizability and stability are examined using quantum mechanics (QM). Intermolecular synthons, hydrogen bonding, crystal morphology and surface chemistry are modelled using empirical force fields. RESULTS: The form I conformation is more stable and polarized with more efficient intermolecular packing, lower void space and higher density, however its shielded hydroxyl is only a hydrogen bond donor. In contrast, the hydroxyl in the more open but less stable and polarized form II conformation is both a donor and acceptor resulting in stronger hydrogen bonding and a more stable crystal structure but one that is less dense. Both forms have strong 1D networks of hydrogen bonds and the differences in packing energies are partially offset in form II by its conformational deformation energy difference with respect to form I. The lattice energies converge at shorter distances for form I, consistent with its preferential crystallization at high supersaturation. Both forms exhibit a needle/lath-like crystal habit with slower growing hydrophobic side and faster growing hydrophilic capping habit faces with aspect ratios increasing from polar-protic, polar-aprotic and non-polar solvents, respectively. Surface energies are higher for form II than form I and increase with solvent polarity. The higher deformation, lattice and surface energies of form II are consistent with its lower solubility and hence bioavailability. CONCLUSION: Inter-relationship between molecular, solid-state and surface structures of the polymorphic forms of ritonavir are quantified in relation to their physical-chemical properties.

Expanding the Repertoire for "Large Small Molecules": Prodrug ABBV-167 Efficiently Converts to Venetoclax with Reduced Food Effect in Healthy Volunteers.

Since gaining approval for the treatment of chronic lymphocytic leukemia (CLL), the BCL-2 inhibitor venetoclax has transformed the treatment of this and other blood-related cancers. Reflecting the large and hydrophobic BH3-binding groove within BCL-2, venetoclax has significantly higher molecular weight and lipophilicity than most orally administered drugs, along with negligible water solubility. Although a technology-enabled formulation successfully achieves oral absorption in humans, venetoclax tablets have limited drug loading and therefore can present a substantial pill burden for patients in high-dose indications. We therefore generated a phosphate prodrug (3, ABBV-167) that confers significantly increased water solubility to venetoclax and, upon oral administration to healthy volunteers either as a solution or high drug-load immediate release tablet, extensively converts to the parent drug. Additionally, ABBV-167 demonstrated a lower food effect with respect to venetoclax tablets. These data indicate that beyond-rule-of-5 molecules can be successfully delivered to humans via a solubility-enhancing prodrug moiety to afford robust exposures of the parent drug following oral dosing.

Novel Physics-Based Ensemble Modeling Approach That Utilizes 3D Molecular Conformation and Packing to Access Aqueous Thermodynamic Solubility: A Case Study of Orally Available Bromodomain and Extraterminal Domain Inhibitor Lead Optimization Series.

Drug design with patient centricity for ease of administration and pill burden requires robust understanding of the impact of chemical modifications on relevant physicochemical properties early in lead optimization. To this end, we have developed a physics-based ensemble approach to predict aqueous thermodynamic crystalline solubility, with a 2D chemical structure as the input. Predictions for the bromodomain and extraterminal domain (BET) inhibitor series show very close match (0.5 log unit) with measured thermodynamic solubility for cases with low crystal anisotropy and good match (1 log unit) for high anisotropy structures. The importance of thermodynamic solubility is clearly demonstrated by up to a 4 log unit drop in solubility compared to kinetic (amorphous) solubility in some cases and implications thereof, for instance on human dose. We have also demonstrated that incorporating predicted crystal structures in thermodynamic solubility prediction is necessary to differentiate (up to 4 log unit) between solubility of molecules within the series. Finally, our physics-based ensemble approach provides valuable structural insights into the origins of 3-D conformational landscapes, crystal polymorphism, and anisotropy that can be leveraged for both drug design and development.

A machine learning framework for computationally expensive transient models.

Transient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

Artificial Intelligence: Can Information be Transformed into Intelligence in Surgical Education?

Artificial intelligence (AI) is being rapidly integrated into various medical applications. Although early application of AI has been achieved in image-based, as well as statistical computational models, translation into procedure-based specialties such as surgery may take longer to achieve. A potential application of AI in surgical education is as a teaching coach or mentor that interacts with the used via virtual and/or augmented reality. The question arises as to whether machines will achieve the wisdom and intelligence of human educators.

Repair of Enlarging Left Ventricular Pseudoaneurysm Two Months After Coronary Artery Bypass Grafting.

Enlarging left ventricular pseudoaneurysms are a rare complication (especially after surgical revascularization) and require tailored surgical decision making and techniques for repair. We present a challenging patient with a rapidly enlarging left ventricular pseudoaneurysm 4 weeks after coronary bypass. The repair was approached through a left thoracotomy using circulatory arrest with selective antegrade cerebral perfusion.

Creation and Global Deployment of a Mobile, Application-Based Cognitive Simulator for Cardiac Surgical Procedures.

Several modern learning frameworks (eg, cognitive apprenticeship, anchored instruction, and situated cognition) posit the utility of nontraditional methods for effective experiential learning. Thus, development of novel educational tools emphasizing the cognitive framework of operative sequences may be of benefit to surgical trainees. We propose the development and global deployment of an effective, mobile cognitive cardiac surgical simulator. In methods, 16 preclinical medical students were assessed. Overall, 4 separate surgical modules (sternotomy, cannulation, decannulation, and sternal closure) were created utilizing the Touch Surgery (London, UK) platform. Modules were made available to download free of charge for use on mobile devices. Usage data were collected over a 6-month period. Educational efficacy of the modules was evaluated by randomizing a cohort of medical students to either module usage or traditional, reading-based self-study, followed by a multiple-choice learning assessment tool. In results, downloads of the simulator achieved global penetrance, with highest usage in the USA, Brazil, Italy, UK, and India. Overall, 5368 unique users conducted a total of 1971 hours of simulation. Evaluation of the medical student cohort revealed significantly higher assessment scores in those randomized to module use versus traditional reading (75% ± 9% vs 61% ± 7%, respectively; P < 0.05). In conclusion, this study represents the first effort to create a mobile, interactive cognitive simulator for cardiac surgery. Simulators of this type may be effective for the training and assessment of surgical students. We investigated whether an interactive, mobile-computing-based cognitive task simulator for cardiac surgery could be developed, deployed, and validated. Our findings suggest that such simulators may be a useful learning tool.

Inheriting the Learner's View: A Google Glass-Based Wearable Computing Platform for Improving Surgical Trainee Performance.

BACKGROUND: It is speculated that, in operative environments, real-time visualization of the trainee's viewpoint by the instructor may improve performance and teaching efficacy. We hypothesized that introduction of a wearable surgical visualization system allowing the instructor to visualize otherwise "blind" areas in the operative field could improve trainee performance in a simulated operative setting. METHODS: A total of 11 surgery residents (4 in general surgery training and 7 in an integrated 6-year cardiothoracic surgery program) participated in the study. Google (Mountain View, CA) Glass hardware running proprietary software from CrowdOptic (San Francisco, CA) was utilized for creation of the wearable surgical visualization system. Both the learner and trainer wore the system, and video was streamed from the learner's system in real time to the trainer, who directed the learner to place needles in a simulated operative field. Subjects placed a total of 5 needles in each of 4 quadrants. A composite error score was calculated based on the accuracy of needle placement in relation to the intended needle trajectories as described by the trainer. Time to task completion (TTC) was also measured and participants completed an exit questionnaire. RESULTS: All residents completed the protocol tasks and the survey. Introduction of the wearable surgical visualization system did not affect mean time to task completion (278 ± 50 vs. 282 ± 69 seconds, p = NS). However, mean composite error score fell significantly once the wearable system was deployed (18 ± 5 vs. 15 ± 4, p < 0.05), demonstrating improved accuracy of needle placement. Most of the participants deemed the device unobtrusive, easy to operate, and useful for communication and instruction. CONCLUSIONS: This study suggests that wearable surgical visualization systems allowing for adoption of the learner's perspective may be a useful educational adjunct in the training of surgeons. Further evaluations of the efficacy of wearable technology in the operating room environment are warranted.