Systems and methods for kinetic response quantification of tetanized skeletal tissue

Abstract

A first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period is obtained. One or more transformed waveforms are generated from the first waveform. The one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period. Change points are identified within the one or more transformed waveforms. The change points occur at a plurality of time points within the first time period. A kinetic contractility vector is generated from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points. The kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period. An effect is determined based on the kinetic contractility vector.

Description

[0001] SYSTEMS AND METHODS FOR KINETIC RESPONSE QUANTIFICATION OF TETANIZED SKELETAL TISSUE

[0002] BACKGROUND

[0003] Skeletal muscle contraction is triggered by an action potential initiated by motor neurons that subsequently propagates along the muscle cell plasma membrane. An important hallmark of skeletal muscle is the ability to generate tetanus, which consists in the muscle’s ability to sustain a contraction caused by action potentials occurring at high rate. As compared to twitch (evoked by low frequency action potentials), tetanus is more physiological relevant.

[0004] As such, there is a need for automated approaches which can non-invasively quantify the kinetic parameters of skeletal muscle undergoing tetanic contraction.

[0005] SUMMARY OF DISCLOSURE

[0006] According to an aspect of the present disclosure there is provided a method for force-based kinetic response quantification of tetanized skeletal tissue. The method comprises obtaining, by one or more processors, a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period, generating, by the one or more processors, one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period, identifying, by the one or more processors, a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period, generating, by the one or more processors, a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period, and determining, by the one or more processors, an effect associated with the first set of conditions based on the kinetic contractility vector. According to a further aspect of the present disclosure there is provided a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to obtain a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period, generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period, identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period, generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period, and determine an effect associated with the first set of conditions based on the kinetic contractility vector.

[0007] According to another aspect of the present disclosure there is provided a device comprising one or more processors and a memory storing instruction which, when executed by the one or more processors, cause the device to obtain a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period, generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period, identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period, generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period, and determine an effect associated with the first set of conditions based on the kinetic contractility vector.

[0008] According to an additional aspect of the present disclosure there is provided a system for force-based kinetic response quantification of tetanized skeletal tissue, the system comprising a bioreactor and a virtual assay unit. The bioreactor comprises a device configured for growing tissue, and a sensor assembly arranged to detect one or more kinetic responses of a skeletal tissue within the device. The virtual assay unit is communicatively coupled to the bioreactor and comprises one or more processors configured to obtain, from the bioreactor, a first waveform comprising a force response of the skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period, generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period, identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period, generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterize the force response of the skeletal tissue over the first time period, determine an effect associated with the first set of conditions based on the kinetic contractility vector, and output the effect associated with the first set of conditions.

[0009] Further aspects and embodiments of the present disclosure are set out in the appended claims.

[0010] In accordance with the above, and with the disclosure herein at least for some implementations, the present disclosure includes applying certain features or aspects with or by use of, a particular machine, e.g., a bioreactor. In various aspects, the bioreactor may comprise a device configured for growing tissue (e.g., human body tissue such as muscle tissue, cardiac tissue, and / or skeletal muscle tissue). A bioreactor as described herein may comprise a sensor assembly configured to obtain tissue activity images of a tissue within the device.

[0011] Further, in accordance with the above, and with the disclosure herein, the present disclosure includes effecting a transformation or reduction of a particular article to a different state or thing, e.g., the transformation or reduction of force response of human skeletal tissue, e.g., within a bioreactor as sensed by a sensor assembly, to a different state or thing, e.g., the generation or creation of a quantifiable, comparable, and discriminative descriptor or feature vector of the kinetic response of the skeletal tissue. Such descriptors or feature vectors may be used within a number of downstream tasks for disease research, drug discovery and / or drug development. For example, a kinetic contractility vector for a skeletal tissue under a first set of conditions (e.g., a perturbation such as application of a drug or compound) may be utilized as part of a virtual assay to predict an effect (e.g., efficacy, toxicity, mechanism of action, etc.) associated with the first set of conditions. The present disclosure therefore provides improvements to drug discovery / development and disease research by providing a low complexity (efficient), comparable, and discriminative quantification of kinetic response which may be used to predict the biological activity of a set of conditions without performing physical experiments. The virtual assay may be deployed on an underlying computing device or system, thereby improving the accuracy and prediction in performing drug discovery / development and / or disease research tasks as described herein. In addition, because the descriptors used by the virtual assay are linked to the underlying dynamics of the tissue activity, they may be used to provide a physiological, biological, or electrochemical explanation to why a set of conditions have been predicted to be associated with a certain effect. This in turn may help to improve understandingwithin the disease research, drug discovery, or drug development tasks described herein.

[0012] Still further, the present disclosure includes improvements in computer functionality or in improvements to other technologies at least because the disclosure herein discloses a process for quantification of kinetic response of tetanized skeletal tissue. The process for quantification of kinetic response, when deployed on the underlying system, allows the systems and methods of the present disclosure to execute with fewer iterations, and use fewer computing resources, than prior art related systems and methods. That is, the present disclosure describes improvements in the functioning of the computer itself of “any other technology or technical field” because the increased discriminative power and predictive accuracy provided by the kinetic contractility vectors and / or features of the present disclosure allows the underlying computer system to utilize less processing and memory resources compared to prior art systems and methods. This is at least because the vectors and / or features provide a compact and discriminative quantification of kinetic response without the need for further transformation, analysis, or computer simulations across a wide range of tests using multiple compute cycles and data. Therefore, use of the kinetic contractility vectors and / or features of the present disclosure results in fewer compute cycles, or otherwise iterations, that has less of an impact on the underlying computing device compared to previous prior art systems and methods.

[0013] Still further, the present disclosure includes specific features other than what is well- understood, routine, conventional activity in the field, and / or otherwise adds unconventional steps that confine the disclosure to a particular useful application, e.g., systems and methods for force-based kinetic response quantification of tetanized skeletal tissue, generating kinetic contractility vectors, and / or predicting an effect associated with a set of conditions (e.g., as related to disease research, drug discovery, and / or drug development).

[0014] Advantages will become more apparent to those of ordinary skill in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

[0015] BRIEF DESCRIPTION OF DRAWINGS

[0016] Embodiments of the present disclosure will now be described, by way of example only, and with reference to the accompanying drawings, in which:

[0017] Figure 1 shows four graphical representations of skeletal muscle activity;

[0018] Figure 2 illustrates the force response of a skeletal tissue undergoing a tetanic contraction during a first time period according to embodiments of the present disclosure;

[0019] Figure 3A shows the force response waveform shown in Figure 2 along with two transformed waveforms according to embodiments of the present disclosure;

[0020] Figure 3B shows the force response waveform shown in Figure 2 along with two further transformed waveforms according to embodiments of the present disclosure;

[0021] Figure 4 shows experimental results quantifying changes in kinetic response of tissues under differing conditions; Figure 5 shows a method for force-based kinetic response quantification of tetanized skeletal tissue according to an aspect of the present disclosure;

[0022] Figure 6 shows a method for determining an effect associated with one or more conditions according to an embodiment of the present disclosure;

[0023] Figure 7 shows a bioreactor system according to embodiments of the present disclosure; and

[0024] Figure 8 shows an example computing system for carrying out the methods of the present disclosure.

[0025] TECHNICAL FIELD

[0026] The present disclosure relates to signal processing of tetanic response waveforms. Particularly, but not exclusively, the present disclosure relates to the extraction of biologically relevant features from force response waveforms of skeletal tissue undergoing a tetanic contraction; more particularly, but not exclusively, the present disclosure relates to the use of such biologically relevant features within in silico, or virtual assays.

[0027] DETAILED DESCRIPTION

[0028] Tetanus consists in a skeletal muscle’s ability to sustain a contraction evoked by the emission of action potentials at a very high rate by the innervating motor neuron. In contrast to twitch (evoked by low frequency action potentials), tetanus may be a more physiological relevant response because it represents a sustained and prolonged contraction of the muscle rather than a single short, and potentially weak, response.

[0029] Figure 1 shows four graphical representations of skeletal muscle activity.

[0030] Figure 1 A shows a graphical representation (myogram) of a single twitch of a skeletal muscle, Figure 1 B shows a graphical representation (myogram) of a wave summation of skeletal muscle contraction, Figure 1 C shows a graphical representation (myogram) of an unfused tetanic contraction of a skeletal muscle, and Figure 1 D shows a graphical representation (myogram) of a fused tetanic contraction of a skeletal muscle. In Figure 1 A, a single stimulus (as represented by the vertical arrow underneath the horizontal axis of Figure 1 A) causes the skeletal muscle to undergo biochemical and physiological events that result in a single contraction (i.e., a single twitch). This is represented in Figure 1 A by the curve with a single peak, the rise of which corresponds to the contraction phase and the fall of which corresponds to the relaxation phase. In Figure 1 B a second stimulus, as indicated by the second vertical arrow under the horizontal axis in Figure 1 B, is applied before the skeletal muscle has fully relaxed from the first stimulus. This results in a subsequent increase in the force of the contractile response of the skeletal muscle (as shown by the second, higher, peak in Figure 1 B). Figure 1 C shows an unfused tetanic contraction where the skeletal muscle is repeatedly and rapidly stimulated, as indicated bythe vertical arrows under the horizontal axis of Figure 1 C, leading to a series of muscle twitches. As in the case of the wave summation shown in Figure 1 B, the skeletal muscle does not fully relax before a subsequent stimulus is received leading to an increase in the force of contraction. However, the skeletal muscle still has time to relax partially before the next stimulus is received and the next contraction occurs. In contrast, Figure 1 D shows a fused tetanic contraction whereby the repeated and rapid stimulation, as indicated bythe vertical arrows under the horizontal axis of Figure 1 D, result in a continuous, sustained, contraction. As shown in Figure 1 D, in the case of fused tetanus (or complete tetanus), the contractions, or twitches, “fuse” together to form a single, smooth, sustained contraction with no visible relaxation between individual twitches.

[0031] To automate the quantification of a skeletal muscle’s tetanic contraction (e.g., within a virtual or in silico assay), the kinetic contractility features of the tetanic contraction may be extracted from a force-response waveform of the tetanic contraction such as that shown in Figure 1 D. However, due to the dynamic response of tetanized skeletal tissue it may be difficult to extract kinetic parameters non-invasively and without supervision (e.g., without manual intervention / measurements and / or without knowing the time and frequency of simulation). The systems and methods of the present disclosure are directed to accurate featurization of tetanic contractions which provide an unsupervised and non-invasive quantification of the kinetic parameters of contractility. Such a featurization generates a direct and comparable metric of tissue phenotype under varying conditions which can be used across a range of tasks such as disease research, drug discovery, and drug development. Figure 2 illustrates a plot 200 of a force response of a skeletal tissue undergoing a tetanic contraction during a first time period according to embodiments of the present disclosure.

[0032] The plot 200 comprises a force response waveform 202 of the skeletal tissue over a first time period and a plurality of change points including a first change point 204, a second change point 206, a third change point 208, a fourth change point 210, and a fifth change point 212. The force response waveform 202 comprises a low-pass filtered representation of a force response of the skeletal tissue undergoing a tetanic contraction during the first time period. The plurality of change points within the force response waveform 202, which may be alternatively referred to as key points, may be used to characterize the force response of the skeletal tissue over the first time period. As will be described in more detail below, the characterization of feree response provided by such change points may be used in a number of downstream tasks such as disease research, drug discovery, and drug development.

[0033] The force response waveform 202 may be obtained from a bioreactor which contains the skeletal tissue (e.g., the bioreactor 702 shown in Figure 7). In the example shown in Figure 2, the force response waveform 202 corresponds to a filtered, or noise-reduced, representation of a raw force response waveform obtained from a bioreactor. More particularly, the force response waveform 202 is generated from the raw force response waveform by applying a noise reduction process (e.g., a low-pass filter) to the raw force response waveform. In one embodiment, the low-pass filter is a moving average filter.

[0034] The first change point 204 corresponds to the rise point of the force response waveform 202. In the context of the present disclosure, a rise point corresponds to a point within a waveform of force response at which the waveform transitions from a baseline level or state to a contracting state — i.e., the point at which the waveform begins to rise. Put another way, the first change point 204 is indicative of (is associated with or represents) a change in the force response waveform 202 from an initial baseline portion to a first contracting (rising) portion. In the example shown in Figure 2, the first change point 204, and thus the rise point, occurs at time point ta. As will be described in more detail below in relation to Figure 3A, the first change point 204 may be determined from a transformed waveform generated from the force response waveform 202. From a physiological perspective, the first change point 204 (rise point) corresponds to the point at which a contractile response by the skeletal tissue is first observed in consequence of a stimulus (i.e., the start of the muscle contraction triggered by an action potential that subsequently propagates along the muscle cell plasma membrane).

[0035] The second change point 206 corresponds to the peak amplitude of the force response waveform 202. That is, the second change point 206 corresponds to the point at which the force response waveform 202 is at or near maximum or peak amplitude such that the second change point 206 is indicative of (is associated with or represents) a maximum force response of the skeletal tissue during the tetanic contraction. In the example shown in Figure 2, the second change point 206, and thus the peak amplitude, occurs at time point tc. As will be described in more detail below in relation to Figure 3A, the second change point 206 may be determined from a transformed waveform generated from the force response waveform 202. From a physiological perspective, the second change point 206 (peak amplitude) corresponds to the approximate peak, or maximum, contractile (force) response of the skeletal tissue during the tetanic contraction whereby the muscle tension is at its maximum (i.e., the crossbridges formed by the myosin heads are producing maximal force).

[0036] The third change point 208 corresponds to the relaxation start point of the force response waveform 202 (alternatively referred to as the fatigue point). In the example shown in Figure 2, the third change point 208, and thus the relaxation start point, occurs at time point td. As will be described in more detail below in relation to Figure 3B, the third change point 208 may be determined from a transformed waveform generated from the force response waveform 202. From a physiological perspective, the third change point 208 (relaxation start point) corresponds to the cessation of applied stimulation. As such, identifying the third change point 208 provides an unsupervised and automated mechanism for determiningthe time point at which stimulation to the skeletal tissue ceased thereby providing additional physiological insight.

[0037] The fourth change point 210 corresponds to the point at which the force response of the skeletal tissue reaches a predetermined proportion of the maximum force response. In the example shown in Figure 2, the fourth change point 210 occurs at time point tband corresponds to the point at which the tetanic contraction of the skeletal tissue reaches 10% of the maximum force of the tetanic contraction. The fourth change point 210 may thus be referred to as a fractional change point, a fractional rise point, or more particularly with respect to the example in Figure 2, the 10% rise point. As will be described in more detail below in relation to Figure 3A, the fourth change point 210 may be determined from a transformed waveform generated from the force response waveform 202. Moreover, whilst the fourth change point 210 corresponds to the point at which the tetanic contraction of the skeletal tissue reaches 10% of the maximum force, other fractions or percentages may be used (e.g., 25%, 50%, etc.). Additionally, whilst Figure 2 shows only a single fractional rise point, multiple fractional rise points may be identified at different fractions / percentages (as discussed in more detail below).

[0038] The fifth change point 212 corresponds to the decay point of the force response waveform 202. In the context of the present disclosure, a decay point corresponds to a point within a waveform of force response at which the force response has decayed by a predetermined proportional amount after the relaxation start point (i.e. , after the third change point 208). In the example shown in Figure 2, the fifth change point 212, and thus the decay point, occurs at time point teand corresponds to the point at which the force response has decayed by 50%. As will be described in more detail below in relation to Figure 3B, the fifth change point 212 may be determined from a transformed waveform generated from the force response waveform 202. Moreover, whilst the fifth change point 212 corresponds to the force decaying by 50%, other percentages may be used (e.g., 25%, 75%, 90%, etc.).

[0039] According to an aspect of the present disclosure, the above described change points (or key points) are used to generate a kinetic contractility vector which characterizes the kinetic response (force response) of the skeletal tissue over the first time period. More particularly, the kinetic contractility vector comprises one or more relative change values (alternatively referred to as kinetic contractility parameter values) calculated from the above described change points such that the one or more relative change values characterize the kinetic response of the tetanic contraction of the skeletal tissue over the first time period. A kinetic contractility vector may be alternatively referred to as a response vector, a feature vector, or a relative change vector.

[0040] The first relative change value, , shown in Figure 2 corresponds to the peak amplitude value of the force response waveform 202 (i.e., the peak force value). The first relative change value <51is calculated as the difference between a baseline force value and the force value associated with the second change point 206 (i.e., the peak amplitude). The baseline value corresponds to the value of the waveform which approximates the force response when the skeletal tissue is not undergoing a tetanic contraction. The baseline value is calculated as the average (e.g., mean or median) of the force response waveform 202 up to the first change point 204 (rise point). In the example shown in Figure 2, the baseline value is calculated as the average of the portion of the force response waveform 202 shown prior to the first change point 204. Alternatively, the baseline value is calculated as the force response value associated with the first change point 204 (i.e., the value of the force response waveform 202 at time point ta).

[0041] The second relative change value, 82, corresponds to the rise time of the force response waveform 202. The second relative change value 82is calculated as the absolute difference between times points tband tc. That is, the second relative change value 82corresponds to the time between the fourth change point 210 (fractional rise point) and the second change point 206 (peak amplitude). The second relative change value 82is determined from the fourth change point 210 (fractional rise point) and not the first change point 204 (rise point) in order to reduce the effect of noise and / or artefacts. For example, electrophysiological outputs from some sharp electrodes pick up the applied stimulation (e.g., an external electrical stimulation applied to the skeletal tissue) which can be inseparable from the first change point 204. Determining the second relative change value 82from a fractional change point after the first change point 204 (e.g., the fourth change point 210) helps to reduce the effect that such noise has.

[0042] In one embodiment, multiple relative change values related to the rise-time of the force response waveform 202 are calculated. The second relative change value, 82, corresponds to the rise-time as measured from the 10% fractional rise point (i.e., from the fourth change point 210). Other relative change values relating to the rise-time may be determined from other fractional rise points (e.g., a rise-time from the 25% fractional rise point, a rise-time from the 50% fractional rise point, etc.). Beneficially, this allows the effect that different compounds or disorders have on the rise (contraction) at different times to be quantified. Such differences may arise due to differences in activation or inactivation of different channels which regulate the rise at different specific times during the contraction.. The third relative change value, <53, corresponds to the fused tetanus time of the force response waveform 202 (alternatively referred to as the fatigue time or the tetanic stimulation time). The third relative change value <53is calculated as the absolute difference between times points tcand td. That is, the third relative change value <53corresponds to the time between the second change point 206 (peak amplitude) and the third change point 208 (relaxation start point). The third relative change value may thus be considered to represent the time period when the skeletal tissue undergoes fused tetanus whilst simulation is applied.

[0043] The fourth relative change value, <54, corresponds to the decay time of the force response waveform 202. The fourth relative change value <54is calculated as the absolute difference between times points tdand te. That is, the fourth relative change value <54corresponds to the time between the third change point 208 (relaxation start point) and the fifth change point 212 (decay point).

[0044] The fifth relative change value, <55, corresponds to the fatigue of the skeletal tissue captured within the force response waveform 202. The fifth relative change value <55is calculated as the absolute difference between the force response associated with the second change point 206 (i.e., the peak amplitude at time point tc) and the force response associated with the third change point 208 (i.e., the relaxation start point at time point td).

[0045] The sixth relative change value, <56, corresponds to the fatigue rate of the skeletal tissue and is calculated as the fatigue of the skeletal tissue divided by the fused tetanus time. That is, the sixth relative change value <56is calculated by dividing the fifth relative change value <55by the third relative change value <53. The sixth relative change value <56provides an indication of the rate at which the skeletal tissue fatigued.

[0046] A kinetic contractility vector, v, comprises all relative change values such that v = {<5=1where n is the number of relative change values (e.g., n = 6). Alternatively, a kinetic contractility vector may comprise one or more of the above relative change values. As such, a kinetic contractility vector characterizes, or encodes, the dynamic behavior of a skeletal tissue as it undergoes a tetanic contraction. Moreover, the kinetic contractility vector provides a low complexity, accurate, and physiologically linked representation of the kinetics of a skeletal tissue as it undergoes a tetanic contraction. The kinetic contractility vector thus provides a non-invasive and discriminative representation of the tetanic contraction which can be easily and efficiently incorporated into a number of downstream tasks such as disease research, drug discovery, and drug development.

[0047] In one embodiment, the kinetic contractility vector further comprises a baseline value and a rise slope value. The baseline value corresponds to an estimation of the force response of the skeletal tissue at a baseline level (i.e., when the tissue is not undergoing a tetanic contraction). The baseline value may be calculated using the first change point 204. More particularly, and as described above, the baseline value is calculated as the average value of the force response waveform 202 prior to the time point tawhich is associated with first change point 204. The rise slope value corresponds to an estimation of the rate of change of the contractile response (i.e., the rise) of the tetanic contraction. The contractile response of the tetanic response corresponds to the portion of the force response waveform 202 between the time points taand tc. The rise slope value may be calculated as the rate of change, or derivative, of the portion of the force response waveform 202 between the time points taand tc(or the portion of the force response waveform 202 between the time points tband tc).

[0048] Additionally, or alternatively, the kinetic contractility vector further comprises a maximum rise slope value. The maximum rise slope value corresponds to the maximum rate of change during contraction of the skeletaltissue. The maximum rise slope value is calculated as the maximum gradient of the force response waveform 202 between the time points taand tc. In one embodiment, the maximum gradient is calculated by finding the maximum gradient of all gradients calculated from the force response waveform 202 at each discrete time point between the first change point 204 (at time point ta) and the second change point 206 (at time point tc).

[0049] Additionally, or alternatively, the kinetic contractility vector further comprises a constriction lag value. The constriction lag value corresponds to the lag, or delay, between a stimulation being applied to the skeletal tissue and the time at which a response to the stimulation is observed in the force response waveform 202. The constriction lag is calculated as the absolute difference between t0Nand tawhere t0Nis the time point at which the stimulation was applied (e.g., by an external electrical stimulation unit). Additionally, or alternatively, the kinetic contractility vector further comprises a relaxation lag value. The relaxation lag value corresponds to the lag, or delay, between the stimulation to the skeletaltissue ceasing and the time at which a response to the cessation of the stimulation is observed in the force response waveform 202. The relaxation lag value is calculated as the absolute difference between t0FFand tdwhere t0FFis the time point at which the stimulation was turned off (e.g., by an external electrical stimulation unit). Typically, the constriction lag value and the relaxation lag value are determined in situations where the skeletal tissue is subjected to external electrical stimulation (e.g., within a bioreactor environment such as that described in relation to Figure 7 below).

[0050] According to an aspect of the present disclosure, the relative change points shown in Figure 2 and described above are identified from at least one transformed waveform generated from the force response waveform.

[0051] Figure 3A shows the force response waveform 202 shown in Figure 2 along with two transformed waveforms according to embodiments of the present disclosure.

[0052] Figure 3A shows a force response waveform 302 over a first time period, a first transformed waveform 304, and a second transformed waveform 306. Figure 3A further shows a first change point 308, a second change point 310, and a third change point 312. The force response waveform 302 corresponds to a filtered representation of the force response waveform 202 shown in Figure 2. In the top plot of Figure 3A, the force response waveform 302 is shown along with the unfiltered force response waveform from which it was generated (as described below).

[0053] The first transformed waveform 304 is a first derivative of the force response waveform 302 and thus encodes (represents or captures) a rate of change of the force response waveform 302 overthe firsttime period. The first transformed waveform 304 may alternatively be referred to as a derivative waveform, a rate of change waveform, or a transformed force response waveform. The first transformed waveform 304 is generated from the force response waveform 302 using any suitable differentiation algorithm such as a finite difference method. As is known, the finite difference method is used to approximate the derivative of a function from a set of data points when the exact formula for the function is not known. The finite difference method can also be used to calculate higher order derivatives such as the second derivative, third derivative, and the like. Alternatively, the first transformed waveform 304 may be calculated using symbolic differentiation, automatic differentiation, and the like. The second transformed waveform 306 is the square of the first derivative of the force response waveform 302 which has been normalized to the range [0, 1]. The second transformed waveform 306 thus also encodes (represents or captures) the rate of change of the force response waveform 302 over the first time period. The second transformed waveform 306 is generated by taking the square of the first transformed waveform 304 and subsequently normalizing the waveform to the range [0, 1] .

[0054] As stated above, the first transformed waveform 304 and / or the second transformed waveform 306 may be used to identify one or more of the change points described in relation to Figure 2 (e.g., the rise point, the relaxation start point, etc.).

[0055] The first change point 308 is identified from the first transformed waveform 304 and corresponds to the rise point (i.e. , the first change point 204 shown in Figure 2). The first change point 308 is identified according to a first threshold such that the first change point 308 corresponds to the first point of the first transformed waveform 304 that exceeds the first threshold TX. Alternatively, the first change point 308 corresponds to the first point of the first transformed waveform 307 that exceeds the first threshold TX. Here, the skilled person will appreciate that, because the waveforms of the present disclosure comprise temporally ordered sequences of values, a “first point which exceeds a threshold” refers to the first value in a waveform which exceeds the threshold when iterating over the values of the waveform in a temporally increasing order. Furthermore, references to values “before” or “after” a certain value within a waveform are to be understood as values which appear temporally before or after the certain value. The first threshold may be manually selected or determined using an automatic validation / testing approach such as cross validation. In one implementation,

[0056] The second change point 310 is identified from the second transformed waveform 306 and corresponds to the peak amplitude (i.e., the second change point 206 shown in Figure 2). The second change point 310 is identified according to a second threshold T2such that the second change point 310 corresponds to the first point after the first change point 308 which is less than the second threshold T2. The second threshold T2may be manually selected or determined using an automatic validation / testing approach such as cross validation. In one implementation, T2= 5 X 10-5. The third change point 312 is identified from the second transformed waveform 306 and corresponds to the relaxation start point (i.e., the third change point 208 shown in Figure 2). The third change point 312 is identified according to a third threshold T3such that the third change point 312 corresponds to the first point after the second change point 310 which exceeds the third threshold T3. The third threshold T3may be manually selected or determined using an automatic validation / testing approach such as cross validation. In one implementation, T3= 1 X 10-3.

[0057] Figure 3B shows the force response waveform 202 shown in Figure 2 along with two further transformed waveforms according to embodiments of the present disclosure.

[0058] Figure 3B shows the force response waveform 302 shown in Figure 3A, a third transformed waveform 314, and a portion of a fourth transformed waveform 316. Figure 3B further shows a fourth change point 318 and a fifth change point 320.

[0059] The third transformed waveform 314 corresponds to a normalized representation of the force response waveform 302. Specifically, the third transformed waveform 314 is generated by normalizing the force response waveform 302 between [0,1] such that the minimum of the third transformed waveform 314 corresponds to the baseline of the force response waveform 302 calculated at the rise point (i.e., the first change point 204 at time point tashown in Figure 2) and the maximum of the third transformed waveform 314 corresponds to the peak amplitude (i.e., the second change point 206 at time point tcshown in Figure 2).

[0060] The fourth transformed waveform 316 corresponds to a normalized representation of the force response waveform 302. The skilled person will appreciate that only a portion of the fourth transformed waveform 316 is shown in Figure 3B for ease of representation and display. The fourth transformed waveform 316 is generated by normalizing the force response waveform 302 between [0,1] such that the minimum of the fourth transformed waveform 316 corresponds to the baseline (as described above) and the maximum of the fourth transformed waveform 316 corresponds to the relaxation start point (i.e., the third change point 208 at time point tdshown in Figure 2).

[0061] The fourth change point 318 is identified from the third transformed waveform 314 and corresponds to the 10% rise point (i.e., the fourth change point 210 shown in Figure 2). The fourth change point 318 is identified using a fourth threshold T4such that the fourth change point 318 is the first point in the third transformed waveform 314 which exceeds T4. The fourth threshold T4may be manually selected or determined using an automatic validation / testing approach such as cross validation. In one implementation, T4= 0.1 such that the fourth change point 318 corresponds to the point at which the force response reaches 10% of the maximum tetanic response as determined by the force response at the peak amplitude (i.e., the force response at time point tc).

[0062] The fifth change point 320 is identified from the fourth transformed waveform 316 and corresponds to the decay point (i.e., the fifth change point 212 shown in Figure 2). The fifth change point 320 is identified using a fifth threshold T5such that the fifth change point 320 is the first point in the fourth transformed waveform 316, after the time point associated with the relaxation start point (td), which is less than T5. The fourth threshold T5may be manually selected or determined using an automatic validation / testing approach such as cross validation. In one implementation, T5= 0.5 such that the fifth change point 320 corresponds to the point at which the force response decays to 50% of the contractile force determined at the relaxation start point.

[0063] Figure 4 shows experimental results quantifying changes in kinetic response of tissues under differing conditions.

[0064] The results shown in Figure 4 correspond to relative change values — i.e., the kinetic response parameters of rise time and decay time — obtained from 3D skeletal tissue under exposed to two compounds with known mechanisms of action (Clenbuterol and Dexamethasone). Each plot A-D within Figure 4 shows a relative change value for an unstimulated (“U”) and a stimulated (“S”) tissue exposed to a given compound. Here, a stimulated tissue is a tissue which was “exercised” during maturation by the application of an external electrical stimulation, whilst an unstimulated tissue received no such stimulation during maturation. The relative change values are shown relative to control values; i.e., the relative change between a kinetic parameter (e.g., rise time) obtained from a vehicle treated tissue and the kinetic parameter obtained from the tissue exposed to a compound. The kinetic parameters were measured over a two week period according to the methods of the present disclosure. The change in kinetic response of a tissue across different conditions reveals the effect associated with the change in conditions. For example, the results indicate that 2 week- dexamethasone treatment results in a dramatic decrease in decay time to 50% in unstimulated tissues (Figure 4D), without affecting rise time in either stimulated or unstimulated tissues (Figure 4B). If a tissue exposed to a compound with an unknown mechanism of action is determined to have a similar kinetic response to that of a tissue exposed to dexamethasone, then it may be inferred that the compound has a similar mechanism of action to dexamethasone. The automatically generated kinetic response vectors (i.e., kinetic contractility vectors) of the present disclosure therefore provide an effective approach for directly comparing and determining effects associated with different conditions (e.g., perturbations such as drug dosages, disease conditions, etc.).

[0065] Advantageously, the systems and methods of the present disclosure automatically extract pharmacologically predictive representations of a skeletal tissue’s response to a tetanic contraction. Automating the extraction of the kinetic parameters of tetanized skeletal tissue allows for data to be collected at scale thereby improving the efficiency of disease research and drug discovery / development processes.

[0066] The description will now turn to methods for utilizing the above described features for forcebased kinetic response quantification.

[0067] Figure 5 shows a method 500 for force-based kinetic response quantification of tetanized skeletal tissue according to an aspect of the present disclosure.

[0068] The method 500 comprises the steps of obtaining 502 a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction, generating 504 a second waveform from the first waveform, identifying 506 change points within the second waveform, the change points occurring at a plurality of time points, generating 508 a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, and determining 510 an effect associated with condition of the skeletal tissue based on the kinetic contractility vector. The method 500 optionally comprises the step of outputting 512 the kinetic contractility vector and / or the effect. In general, the method 500 generates a low complexity, discriminative, and physiologically linked (i.e., explainable) representation of the kinetic response of a skeletal tissue as it undergoes a tetanic contraction. The method 500 generates a feature vector (kinetic contractility vector) from a force response waveform, or signal, of the skeletal tissue undergoing a tetanic contraction. The feature vector comprises one or more values which are linked to physiological characteristics of the kinetics of the tetanic contraction. These kinetic parameters are identified non-invasively and without supervision. Thus, the feature vector provides an explainable quantification of the force response of the skeletal tissue during the tetanic contraction whilst also being directly comparable to other such feature vectors thereby allowing effects associated with conditions of the skeletal tissue (e.g., toxicity of a compound applied to the skeletal tissue, efficacy of a compound applied to the skeletal tissue, etc.) to be efficiently and accurately identified.

[0069] At the step of obtaining 502, a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period is obtained. The skeletal tissue is under a first set of conditions during the first time period. The first set of conditions are either reference conditions or perturbed conditions associated with one or more perturbations such as a drug treatment, a disease model, a cell line, or a physical perturbation.

[0070] Obtaining the first waveform may comprise opening or reading a representation of the waveform from a persistent storage location. Alternatively, obtaining the first waveform may comprise receiving the first waveform from a bioreactor or other device containing the skeletal tissue or operable to obtain measurements (e.g., force-based measurements) from the skeletal tissue. An example bioreactor is shown in Figure 7.

[0071] In one embodiment, the skeletal tissue is an engineered, or artificial, skeletal tissue which may be held and / or grown within a bioreactor or similar device. In one embodiment, the skeletal tissue is an artificial 3D skeletal muscle tissue comprising a hydrogel, a plurality of cells that includes skeletal muscle cells, and two or more anchors. In some embodiments, the plurality of cells further comprises fibroblasts. In some embodiments, the fibroblasts and the skeletal muscle cells are at a ratio of between about 1 :5 and 1 :50. In some embodiments, the skeletal muscle cells comprise human skeletal muscle cells. In some embodiments, the hydrogel comprises collagen or a collagen derivative, intestinal submucosa or a derivative thereof, cellulose or a cellulose derivative, a proteoglycan, heparin sulfate, chondroitin sulfate, keratin sulfate, hyaluronic acid, elastin, fibronectin, laminin, fibrin, chitosan, alginate, Matrigel®, Geltrex, agarose, decellularized extracellular matrix, polyethylene glycol or a derivative thereof, silicone or a derivative thereof, or a combination thereof. In some embodiments, the hydrogel comprises collagen or a collagen derivative. In some embodiments, the hydrogel comprises Matrigel®. In some embodiments, the collagen comprises Type I collagen, Type III collagen, Type IV collagen, Type V collagen, Type XI collagen, Type XII collagen, or a combination thereof. In some embodiments, at least a portion of the cells are encapsulated or embedded inside the hydrogel.

[0072] In one embodiment, the method 500 further comprises the step of filtering (not shown) the first waveform using a low-pass filter to generate a filtered first waveform. In such embodiments, further steps which make use of the first waveform (e.g., the step of generating 504 described below) make use of the filtered first waveform. In one embodiment, the low-pass filter is a moving average filter.

[0073] At the step of generating 504, one or more transformed waveforms are generated from the first waveform. At least one of the one or more transformed waveforms encodes a rate of change of the first waveform over the first time period.

[0074] The one or more transformed waveforms comprise a first derivative of the first waveform (e.g., the first transformed waveform 304 shown in Figure 3A) and / or a squared first derivative of the first waveform (e.g., the second transformed waveform 306 shown in Figure 3A). Additionally, or alternatively, the one or more transformed waveforms comprise an exponentiation of the first derivative of the first waveform (e.g., the first derivative of the first waveform raised to a power >= 1). The one or more transformed waveforms may further comprise at least one normalized representation of the first waveform (e.g., the third transformed waveform 314 and / or the fourth transformed waveform 316 shown in Figure 3B). Additionally, or alternatively, the one or more transformed waveforms comprise an absolute value representation of the first waveform (e.g., the absolute value of the first waveform), and / or a rectified representation of the first waveform. At the step of identifying 506, a plurality of change points is identified within the one or more transformed waveforms. The plurality of change points occur at a plurality of time points within the first time period.

[0075] The plurality of change points within the force response, which may be alternatively referred to as key points, may be used to characterize the force response of the skeletal tissue over the first time period. As will the plurality of change points within the second waveform include one or more of a rise point indicative of a change in the first waveform from an initial baseline portion to a first rising portion (e.g., the first change point 204 shown in Figure 2 and the first change point 308 shown in Figure 3A), a peak point indicative of a maximum force response of the skeletal tissue during the tetanic contraction (e.g., the second change point 206 shown in Figure 2 and the second change point 310 shown in Figure 3A), a relaxation start point associated with a time at which the skeletal tissue fatigues during the tetanic contraction (e.g., the third change point 208 shown in Figure 2 and the third change point 312 shown in Figure 3A), a fractional rise point indicative of the force response of the skeletal tissue reaching a predetermined proportion of a maximum force response of the skeletal tissue during the tetanic contraction (e.g., the fourth change point 210 shown in Figure 2 and the fourth change point 318 shown in Figure 3B), and a decay point indicative of the force response having decayed by a predetermined proportional amount after the relaxation start point (e.g., the fifth change point 212 shown in Figure 2 and the fifth change point 320 shown in Figure 3B). The skilled person is referred to the above description of Figures 2, 3A, and 3B for details regarding automatically extracting / identifying these change points.

[0076] Advantageously, the change points described above may be identified from within a force response waveform non-invasively and without supervision. That is, the change points can be identified without prior knowledge of the characteristics or properties of the tetanic contraction (e.g., frequency of stimulation, time points associated with individual stimuli, etc.) and without requiring a physical measurement of the tissue which may result in a disturbance of the tissue. Moreover, each of the change points are linked to a specific (physiological) characteristic thereby providing a greater degree of insight into the physiological characteristics of the tetanic response. Such explainability may be particularly advantageous within disease research and drug discovery settings whereby the characteristics of a tissue’s force response may be directly linked to the conditions under which the tissue has been placed. At the step of generating 508, a kinetic contractility vector is generated from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points. The kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period.

[0077] The kinetic contractility vector comprises one or more relative change values (alternatively referred to as kinetic contractility parameter values) determined from the comparison of values of the first waveform determined at the plurality of time points. The one or more relative change values comprise one or more of a peak amplitude of the first waveform (e.g., the first relative change value shown in Figure 2), a rise time of the first waveform (e.g., the second relative change value 82shown in Figure 2), a fused tetanus time of the first waveform(e.g., the third relative change value <53shown in Figure 2), a decay time of the first waveform (e.g., the fourth relative change value <54shown in Figure 2), a fatigue of the skeletal tissue (e.g., the fifth relative change value <55shown in Figure 2), and a fatigue rate of the skeletal tissue (e.g., the sixth relative change value <56shown in Figure 2).

[0078] Beneficially, the kinetic contractility vector provides an efficient, low complexity, representation of the kinetic response of the skeletal tissue during a tetanic contraction. Such an efficient representation reduces storage and memory requirements whilst also allowing for accurate and explainable comparison due to the individual elements of the vector being linked to physiological characteristics of the tissue’s kinetic response. This helps improve the efficiency and accuracy of downstream tasks which employ such vectors (e.g., disease research tasks, drug discovery, etc.) whilst also improving the performance of computers / systems which generate and store such vectors by making more efficient use of compute resources.

[0079] At the step of determining 510 an effect associated with the first set of conditions is determined based on the kinetic contractility vector. The effect is one or more of an efficacy, a toxicity, or a mechanism of action associated with the first set of conditions.

[0080] More details regarding the steps performed at the step of determining 510 the effect are provided below in relation to Figure 6. At the optional step of outputting 512, the kinetic contractility vector and / or the effect associated with the first set of conditions is output.

[0081] In one embodiment, outputtin the kinetic contractility vector and / or the effect associated with the first set of conditions comprises storing, or saving, the kinetic contractility vector and / or the effect associated with the first set of conditions to a persistent storage such as a nonvolatile memory, a non-transitory medium, or the like. Additionally, or alternatively, outputting the kinetic contractility vector and / or the effect associated with the first set of conditions comprises transmitting the kinetic contractility vector and / or the effect associated with the first set of conditions via a network (e.g., a local area network, a wide area network, and the like), or displaying a representation of the kinetic contractility vector and / or the effect associated with the first set of conditions for review by a user. Additionally, or alternatively, outputtingthe kinetic contractility vector comprises providingthe kinetic contractility vector to a step of another method or process (e.g., method 600 shown in Figure 6 and described below).

[0082] In one embodiment, the method 500 comprises, prior to the step of obtaining 502 the first waveform, the step of causing (not shown) stimulation of the skeletal tissue at a predetermined frequency to evoke the tetanic contraction. The predetermined frequency is a frequency from 5Hz to 100Hz.

[0083] Figure 6 shows a method 600 for determining an effect associated with one or more conditions according to an embodiment of the present disclosure.

[0084] The method 600 comprises the steps of identifying 602 a set of kinetic contractility vectors, comparing 604 a first kinetic contractility vector to the set of kinetic contractility vectors to identify a second kinetic contractility vector, and identifying 606 a known effect associated with the second kinetic contractility vector. The method 600 further comprises the optional step of associating 608 the known effect with the skeletal tissue (from which the first kinetic contractility vector was generated). In one embodiment, the method 600 is performed as part of the step of determining 510 in the method 500.

[0085] In general, the method 600 utilizes kinetic contractility vectors to identify effects (e.g., toxicity, efficacy, mechanism of action, etc.) associated with one or more conditions of a skeletal tissue. For example, a database of vectors each having a known effect may be used to identify an unknown effect associated with a kinetic contractility vector based on a similarity between the kinetic contractility vector and vectors within the database. That is, if vector vais generated from a first skeletal tissue under a first set of conditions having a known effect e, then if a second vector vb, generated from a second skeletal tissue under a second set of conditions with an unknown effect is similar to va, then the effect e can be associated with the second set of conditions of the second skeletal tissue.

[0086] At the step of identifying 602, a set of kinetic contractility vectors are identified. Each of the kinetic contractility vector relate to a respective skeletal tissue having one or more conditions associated with a respective known effect.

[0087] The set of kinetic contractility vectors may be stored in a database or other persistent data structure. Each vector within the set of kinetic contractility vectors is associated with a skeletal tissue having one or more known conditions (e.g., a vector generated from a skeletal tissue under reference or baseline conditions, or a vector generated from a skeletal tissue under perturbed conditions). The set of kinetic contractility vectors may be generated from different skeletal tissues under a diverse range of conditions.

[0088] At the step of comparing 604, a kinetic contractility vector is compared to the set of kinetic contractility vectors to identify a related kinetic contractility vector within the set of kinetic contractility vectors.

[0089] The kinetic contractility vector corresponds to a kinetic contractility vector generated as a result of performing method 500 (as described above). Alternatively, the kinetic contractility vector corresponds to a previously generated vector which is obtained from a storage location. In general, the kinetic contractility vector is associated with a set of conditions (e.g., the kinetic contractility vector is generated from a skeletal tissue under a set of conditions) which are unknown conditions or known conditions (e.g., in order to validate the known conditions on the basis of previously observed conditions).

[0090] The step of comparing 604 comprises comparing the kinetic contractility vector to each vector within the set of kinetic contractility vectors. The related contractility vector may then be identified as the vector within the set of kinetic contractility vector which is most similar to the kinetic contractility vector. Here, similarity between two contractility vectors may be calculated using a suitable distance, similarity, or dissimilarity metric such as the L1 norm, L2 norm, Minkowski Distance, Hamming Distance, or the like. Two vectors may be deemed to be similar if the similarity metric between the two vectors is above a predetermined threshold, or the dissimilarity metric between the two vectors is below a predetermined threshold.

[0091] In one embodiment, each kinetic contractility vector is a distribution of values determined from multiple observations of the same tissue under the same conditions. For example, a number of tetanic contractions of a first tissue under a first set of conditions are evoked (e.g., 5 contractions, 10 contractions, 20 contractions, 100 contractions, etc.) and a kinetic contractility vector is generated for each tetanic contraction. The distribution of each kinetic parameter within the generated kinetic contractility vectors is then estimated and the distributions used for the values of the kinetic contractility vector for the first tissue (e.g., each value within the kinetic contractility vector includes a mean and standard deviation of the distribution estimated for that kinetic parameter). Two distribution based kinetic contractility vectors may be compared using any suitable metric such as the Kullback-Leibler divergence or the Jensen-Shannon divergence.

[0092] At the step of identifying 606, a known effect associated with the related kinetic contractility vector is identified. The effect associated with the first set of conditions thus corresponds to the known effect associated with the related or otherwise similar kinetic contractility vector.

[0093] At the option step of associating 608, the known effect is associated with the skeletal tissue from which the kinetic contractility vector is obtained. That is, the known effect is associated with the set of conditions of the skeletal tissue.

[0094] For example, if the skeletal tissue has been dosed with a compound having an unknown mechanism of action and the kinetic contractility vector associated with the skeletal tissue is identified as similar to a vector generated from a tissue having been dosed with a drug having a known mechanism of action, then the known mechanism of action may be associated with the compound by virtue of the similarity between the two kinetic contractility vectors (i.e., because the kinetic contractile responses of the tissues under tetanus are similar). In one embodiment, the steps of comparing 604, identifying 606, and associating 608 are performed by a machine learning model trained to take a kinetic contractility vector as input and output one or more effects. In general, the machine learning model is trained on a data set of kinetic contractility vectors with known outputs. For example, a feedforward neural network comprising an input layer of 6 input nodes (for the 6 elements of the kinetic contractility vector), a first hidden layer comprising 8 nodes, a second hidden layer comprising 4 nodes, and an output layer comprising 2 output nodes may be trained to predict a single binary effect such as toxicity. Such a neural network can be trained using any suitable approach as is known in the art. Moreover, the skilled person will appreciate that any suitable machine learning model can be used to predict one or more effects from an input kinetic contractility vector. In a further embodiment, the steps of comparing 604, identifying 606, and associating 608 are performed using unsupervised clustering (e.g., k-means clustering, Gaussian Mixture Models, etc.). For instance, a dataset of kinetic contractility vectors are clustered into k groups (e.g., k = 2, 3, etc.) and an input kinetic contractility vector is compared to the k cluster centers to determine one or more effects associated with the closest cluster center.

[0095] In one embodiment, the method 600 is performed using relative changes in kinetic contractility vectors from a first set of conditions to a second set of conditions. That is, each kinetic contractility vector within the set of kinetic contractility vectors is calculated by determining the difference between a first kinetic contractility vector corresponding to a skeletal tissue under a first set of conditions (e.g., baseline or control conditions) and a second kinetic contractility vector corresponding to the skeletal tissue under a second set of conditions (e.g., perturbed conditions). Each kinetic contractility vector thus encodes a change in the skeletal tissue as a result of a change from the first set of conditions to the second set of conditions. Within such an embodiment, the first kinetic contractility vector is similarly generated by calculating a difference in two kinetic contractility vectors under two different conditions: a first vector generated from the skeletal tissue under a first set of conditions and a second vector generated from the skeletal tissue under a second set of conditions. Advantageously, using relative change vectors normalizes the response observed in the second set of conditions with respect to the first set of conditions thereby improving the comparability of the kinetic contractility vectors. The skilled person will appreciate that the above describe distribution based approach to generating kinetic contractility vectors may be combined with the relative measurements approach so as to generate relative distributions of kinetic parameters.

[0096] Figure 7 shows a bioreactor system 700 according to embodiments of the present disclosure.

[0097] The bioreactor system 700 comprises a bioreactor 702 and a control unit 704. The bioreactor 702 comprises a device 706 for growing engineered tissues, a sensor assembly 708, and an interface 710. The interface 710 communicatively couples the bioreactor 702 and the control unit 704 such that data may be exchanged between the bioreactor 702 and the control unit 704. The control unit 704 may comprise one or more processors configured to carry out the methods of the present disclosure as described in relation to Figures 5 and 6. As such, the control unit 704 may be configured to operate to act as a virtual assay unit by performing the steps and features of the present disclosure.

[0098] As shown in the expanded portion 706-1 of the device 706, the device 706, or substrate, comprises one or more wells, such as the well 712, one or more cell culture wells, such as the cell culture well 714, a pair of electrodes including a first electrode 716-1 and a second electrode 716-2, and a pair of elements including a first element 718-1 and a second element 718-2. The well 712 is positioned within the cell culture well 714 and has a bottom on the device 706, a first end 720-1 , and a second end 720-2. The well 712 is configured for growing an engineered tissue 722 from cells seeded therein. Culture medium may be added to the cell culture well 714 for growing and / or sustaining the engineered tissue 722. The engineered tissue 722, alternatively referred to as artificial tissue, comprises engineered skeletaltissue. In one embodiment, the skeletaltissue is an artificial 3D skeletal muscle tissue comprising a hydrogel, a plurality of cells that includes skeletal muscle cells, and two or more anchors. In some embodiments, the plurality of cells further comprises fibroblasts. In some embodiments, the fibroblasts and the skeletal muscle cells are at a ratio of between about 1 :5 and 1 :50. In some embodiments, the skeletal muscle cells comprise human skeletal muscle cells. In some embodiments, the hydrogel comprises collagen or a collagen derivative, intestinal submucosa or a derivative thereof, cellulose or a cellulose derivative, a proteoglycan, heparin sulfate, chondroitin sulfate, keratin sulfate, hyaluronic acid, elastin, fibronectin, laminin, fibrin, chitosan, alginate, Matrigel®, Geltrex, agarose, decellularized extracellular matrix, polyethylene glycol or a derivative thereof, silicone or a derivative thereof, or a combination thereof. In some embodiments, the hydrogel comprises collagen or a collagen derivative. In some embodiments, the hydrogel comprises Matrigel®. In some embodiments, the collagen comprises Type I collagen, Type III collagen, Type IV collagen, Type V collagen, Type XI collagen, Type XII collagen, or a combination thereof.

[0099] The pair of electrodes are separated by a gap within which the well 712 is positioned. The pair of electrodes are configured to apply an electrical stimulation to cell cultures within the one or more wells of the device 706 (e.g., the engineered tissue 722 within the well 712 shown in the expanded portion 706-1 ). During maturation of the cell cultures within the device 706, the pair of electrodes apply stimulation to the cell cultures according to a multi-week electrical stimulation protocol. After the cell cultures are matured, the pair of electrodes may be configured to stimulate the cell cultures (e.g., the engineered tissue 722) at a set frequency, or pacing frequency. To evoke tetanus in the engineered tissue 722, the pacing frequency may be set at from 5Hz to 100Hz.

[0100] The first element 718-1 and the second element 718-2 are disposed across the well 712 such that there is a gap between the bottom of the well 712 and the pair of elements. The first element 718-1 and the second element 718-2 are configured to: (a) permit attachment of the engineered tissue 722 formed therebetween, thereby suspending the engineered tissue 722 above the bottom of the well 712, and (b) deform in response to the contractile force exerted on the pair of elements by the engineered tissue 722, thereby simulating a physiological environment that is native to the engineered tissue 722 and / or permitting measurement of the contractile force (e.g., by the sensor assembly 708). For example, the pair of electrodes may subject the engineered tissue 722 to an electrical stimulation at a frequency of 0.1 Hz. The engineered tissue 722 will contract in response to this electrical stimulation causing deformation of at least one of the first element 718-1 and the second element 718-2.

[0101] The sensor assembly 708 is configured to obtain a plurality of images of a tissue (e.g., the engineered tissue 722) at a plurality of time points. For example, the optical sensor may be configured to capture an image, or frame, of a tissue at a predetermined time point (e.g., 1 day post-seeding, 2 days post-seeding, etc.). In one embodiment, the control unit 704 is configured to send a command 724 to the bioreactor 702 which causes the sensor assembly 708 to obtain an image 726 of a tissue (e.g., the engineered tissue 722) which is then returned to the control unit 704. The image 726 includes at least a portion of the first element 718-1 and / or the second element 718-2 such that the deformation of these elements as a result of contraction of the tissue is captured within the image 726 thereby allowing the force response of the contraction to be measured. At least in some implementations, the force response of the contraction may be measured by modelling contractile deflection of flexible tissue scaffolds. For example, in one approach, systems and methods may be implemented to track the deflection of a tissue scaffold and subsequently model the force exerted on the tissue scaffold. Such systems and method are further described by U.S. Provisional Application No. 63 / 526,566 as filed on July 13, 2023, and titled “Systems and Methods for Tissue Scaffold Tracking,” which is incorporated by reference in its entirety herein.

[0102] Figure 8 shows an example computing system for carrying out the methods of the present disclosure. Specifically, Figure 8 shows a block diagram of an embodiment of a computing system according to example embodiments of the present disclosure. The computing system shown in Figure 8 may correspond to a part, or the whole, of any of the functional units described above.

[0103] Computing system 800 can be configured to perform any of the operations disclosed herein such as, for example, any of the operations discussed with reference to the method steps described in relation to Figures 5 and 6. Computing system includes one or more computing device(s) 802. The one or more computing device(s) 802 of computing system 800 comprise one or more processors 804 and memory 806. One or more processors 804 can be any general purpose processor(s) configured to execute a set of instructions, such as computing instructions including implemented in any one or more programming languages such as Python, Go, C, C++, C#, Java, or the like. For example, one or more processors 804 can be one or more general-purpose processors, one or more field programmable gate array (FPGA), and / or one or more application specific integrated circuits (ASIC). In one embodiment, one or more processors 804 include one processor. Alternatively, one or more processors 804 include a plurality of processors that are operatively connected. One or more processors 804 are communicatively coupled to memory 806 via address bus 808, control bus 810, and data bus 812. Memory 806 can be a random access memory (RAM), a read only memory (ROM), a persistent storage device such as a hard drive, an erasable programmable read only memory (EPROM), and / or the like. The one or more computing device(s) 802 further comprise I / O interface 814 communicatively coupled to address bus 808, control bus 810, and data bus 812.

[0104] Memory 806 can store information that can be accessed by one or more processors 804. For instance, memory 806 (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can include computer-readable instructions (not shown) (e.g., computing instructions) that can be executed by one or more processors 804. The computer-readable instructions can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the computer-readable instructions can be executed in logically and / or virtually separate threads on one or more processors 804. For example, memory 806 can store instructions (not shown), such as computing instructions, that when executed by one or more processors 804 cause one or more processors 804 to perform operations such as any of the operations and functions for which computing system 800 is configured, as described herein. In addition, or alternatively, memory 806 can store data (not shown) that can be obtained, received, accessed, written, manipulated, created, and / or stored. The data can include, for instance, the data and / or information described herein in relation to Figures 1 to 4. In some implementations, the one or more computing device(s) 802 can obtain from and / or store data in one or more memory device(s) that are remote from the computing system 800.

[0105] Computing system 800 further comprises storage unit 816, network interface 818, input controller 820, and output controller 822. Storage unit 816, network interface 818, input controller 820, and output controller 822 are communicatively coupled to the central control unit (i.e., the memory 806, the address bus 808, the control bus 810, and the data bus 812) via I / O interface 814.

[0106] Storage unit 816 is a computer readable medium, preferably a non-transitory computer readable medium, comprising one or more programs, the one or more programs comprising computing instructions which when executed by the one or more processors 804 cause computing system 800 to perform the method steps of the present disclosure. Alternatively, storage unit 816 is a transitory computer readable medium. Storage unit 816 can be a persistent storage device such as a hard drive, a cloud storage device, or any other appropriate storage device. Network interface 818 can be a Wi-Fi module, a network interface card, a Bluetooth module, and / or any other suitable wired or wireless communication device. In an embodiment, network interface 818 is configured to connect to a network such as a local area network (LAN), or a wide area network (WAN), the Internet, or an intranet. The above illustrative examples of various aspects and implementations provide an overview for understanding aspects and implementation of the disclosed method. The figures provided herein depict exemplary aspects of the present system and methods and are not intended to limit the scope of the disclosure.

[0107] Unless otherwise stated, all technical terms used herein have the same meaning as commonly understand by a person skilled in the art. Singular forms “a”, “an” and “the” include plural references unless the context of the disclosure clearly dictates otherwise. The term “or” is intended to encompass “and / or” unless clearly stated otherwise.

[0108] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

Claims

CLAIMSWhat is claimed is:1 . A method for force-based kinetic response quantification of tetanized skeletal tissue, the method comprising: obtaining, by one or more processors, a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period; generating, by the one or more processors, one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period; identifying, by the one or more processors, a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period; generating, by the one or more processors, a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period; and determining, by the one or more processors, an effect associated with the first set of conditions based on the kinetic contractility vector.

2. The method of claim 1 further comprising: outputting, by the one or more processors, the kinetic contractility vector and / or the effect associated with the first set of conditions.

3. The method of claim 1 wherein determining the effect further comprises:identifying, by the one or more processors, a set of kinetic contractility vectors each related to a respective skeletal tissue having one or more conditions associated with a respective known effect; comparing, by the one or more processors, the kinetic contractility vector to the set of kinetic contractility vectors to identify a related kinetic contractility vector within the set of kinetic contractility vectors; and identifying, by the one or more processors, a known effect associated with the related kinetic contractility vector, wherein the effect associated with the first set of conditions corresponds to the known effect associated with the related kinetic contractility vector.

4. The method of claim 1 wherein the first set of conditions comprises a reference condition, or a perturbed condition associated with one or more perturbations.

5. The method of claim 4 wherein the one or more perturbations includes one or more of a drug treatment, a disease model, a cell line, or a physical perturbation.

6. The method of claim 1 wherein the effect is an efficacy, a toxicity, or a mechanism of action associated with the first set of conditions.

7. The method of claim 1 wherein the kinetic contractility vector comprises one or more relative change values determined from the comparison of values of the first waveform determined at the plurality of time points.

8. The method of claim 7 wherein the one or more relative change values comprise one or more of a peak amplitude of the first waveform, a rise time ofthe first waveform, a fused tetanus time of the first waveform, a decay time of the first waveform, a fatigue of the skeletal tissue, and a fatigue rate of the skeletal tissue.

9. The method of claim 1 wherein the plurality of change points within the one or more transformed waveforms includes a rise point indicative of a change in the first waveform from an initial baseline portion to a first rising portion.

10. The method of claim 1 wherein the plurality of change points within the one or more transformed waveforms includes a fractional rise point indicative of the force response of the skeletal tissue reaching a predetermined proportion of a maximum force response of the skeletal tissue during the tetanic contraction.11 . The method of claim 1 wherein the plurality of change points within the one or more transformed waveforms includes a peak point indicative of a maximum force response of the skeletal tissue during the tetanic contraction.

12. The method of claim 1 wherein the plurality of change points within the one or more transformed waveforms includes a relaxation start point associated with a time at which the skeletal tissue fatigues during the tetanic contraction.

13. The method of claim 12 wherein the plurality of change points within the one or more transformed waveforms includes a decay point indicative of the force response having decayed by a predetermined proportional amount after the relaxation start point.

14. The method of claim 1 further comprising: filtering, by the one or more processors, the first waveform using a low-pass filter to generate a filtered first waveform, wherein the at least one transformed waveform is generated from the filtered first waveform.

15. The method of claim 1 further comprising, prior to obtaining the first waveform: causing, by the one or more processors, stimulation of the skeletal tissue at a predetermined frequency to evoke the tetanic contraction.

16. The method of claim 15 wherein the predetermined frequency is from 5Hz to 100Hz.

17. The method of claim 1 wherein the one or more transformed waveforms comprise one or more of: a first derivative of the first waveform, a squared first derivative of the first waveform, and at least one normalized representation of the first waveform.

18. The method of claim 1 wherein the skeletal tissue is an engineered skeletal tissue.

19. The method of claim 18 wherein the first waveform is obtained from a bioreactor within which the engineered skeletal tissue is held.

20. A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to: obtain a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period; generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period; identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period; generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletaltissue over the first time period; and determine an effect associated with the first set of conditions based on the kinetic contractility vector.

21. A device comprising: one or more processors; anda memory storing instructions which, when executed by the one or more processors, cause the device to: obtain a first waveform comprising a force response of a skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period; generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period; identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period; generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterizes the kinetic response of the skeletal tissue over the first time period; and determine an effect associated with the first set of conditions based on the kinetic contractility vector.

22. A system for force-based kinetic response quantification of tetanized skeletal tissue, the system comprising: a bioreactor comprising: a device configured for growing tissue; and a sensor assembly arranged to detect one or more kinetic responses of a skeletal tissue within the device; and a virtual assay unit communicatively coupled to the bioreactor and comprising one or more processors configured to:obtain, from the bioreactor, a first waveform comprising a force response of the skeletal tissue undergoing a tetanic contraction during a first time period, wherein the skeletal tissue is under a first set of conditions during the first time period; generate one or more transformed waveforms from the first waveform, wherein the one or more transformed waveforms include at least one transformed waveform which encodes a rate of change of the first waveform over the first time period; identify a plurality of change points within the one or more transformed waveforms, wherein the plurality of change points occur at a plurality of time points within the first time period; generate a kinetic contractility vector from the first waveform based on a comparison of values of the first waveform determined at the plurality of time points, wherein the kinetic contractility vector characterize the force response of the skeletal tissue over the first time period; determine an effect associated with the first set of conditions based on the kinetic contractility vector; and output the effect associated with the first set of conditions.31

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