## 1 Introduction  
Over the last two decades, the use of musculoskeletal (MSK) dynamics models has increased enormously [1] in their ability to estimate biomechanical quantities that may not be readily measured in vivo (e.g., joint contact forces [2, 3]) and supporting clinical-decision making [4, 5]. MSK models, often referred to as digital twins, have been generated to simulate various human movements and predict clinical outcomes, e.g., to address orthopedic research questions [6, 7], design personalized therapy regimens and rehabilitation programs [8], or enhance athletic performance [9]. Despite their promising applications, their actual clinical application is rarely reported [4, 10], because several challenges hinder the widespread adoption of MSK models in clinical practice. For instance, despite the availability of software and workflows to streamline and standardize model development [11, 12], the process to generate a personalized MSK model remains time-consuming (i.e., hours--or even days--are required particularly for the segmentation of the bone/muscle geometries instead of few minutes to scale a generic model) and requires a large volume of data, including medical imaging for each subject. Moreover, a unified approach to develop personalized models is lacking, resulting in large variability in terms of MSK model personalization among centers/groups, which is tightly linked to the amount of available experimental data (or lack thereof)--especially in clinical settings. Such variability likely leads to inconsistencies that can affect the reliability and reproducibility of the models and their estimates [13, 14]. Numerous studies indicate that generic models fail to adequately represent both skeletal and muscular properties of individual subjects, even when scaled to the dimensions of the subject under study, leading to inaccurate predictions [15; 16; 17; 18]. Subject-specific features (e.g., characteristic angles or rotations) are not captured to the point that generic-scaled bones do not well approximate the real bones [19]. In addition, the muscle properties do not scale linearly with the bones [20]. However, since the absence of a standardized procedure, model personalization is left to the modeler's expertise, which does not build trust in the methodology itself [14; 21; 22]. One notable example is the variety of methods employed to define maximal isometric force [23; 24; 25], the choice of which strongly influences the percentage of successful simulations [26]. Another crucial step before a model can be trusted and considered reliable is its validation, which must be performed for the specific motor task being simulated. Validation requires an appropriate number of samples and involves comparing the results of the simulated task with experimental data (from relevant gold standard measures). Additionally, a sensitivity analysis should be conducted to strengthen the validation, ensuring model robustness and reliability. MSK models have been used to simulate the most disparate locomotor tasks, from overground or level walking [22; 27] to running [28; 29], cycling movements [30; 31], or other sports (e.g., rugby [32]). However, to the best of the authors' knowledge, the maximal voluntary isometric contraction (MVIC) test, commonly used to assess quadriceps muscle strength, has seldom been replicated in silico [33; 34]. This gap in the literature highlights the need for a standardized approach to simulate accurately such tests, which are essential for assessing muscle strength and functionality. The aim of the present study was twofold: (1) to develop a robust pipeline for simulating an MVIC of the knee extensors with the future prospect of validation, and (2) to identify the minimum amount of information required to obtain physiologically plausible estimates of the MVIC torque, with a predictive error lower than the intrinsic error of the experimental measurements [35; 36; 37].