FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM FOR DESIGNING BIO-DEGRADABLE POLYMER BASED MEDICAL DEVICES
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The disclosure herein generally relates to design of devices, and, more particularly, to a method and system for designing bio-degradable polymer based medical devices.
BACKGROUND
[002] Biodegradable polymers are extensively used as key constituents in medical devices and in other applications. Such medical devices can be used in bio-implants, tissue engineering, artificial bones, for controlled drug delivery, and so on. The performance characteristics of the final product, i.e. the medical device, depend on the materials and their inherent properties used in the formulation of the device such as but not limited to polymers used for making the device, blending ratios of different polymers used, molecular weight, crystallinity, and polydispersity of each polymer, and shape and dimensions of the device. Such characteristics of the polymer(s) used, have an impact on the performance of the medical device such as but not limited to lifespan of the device, kinetics of drug release, and structural properties.
[003] Each medical device has to meet desired performance characteristics so as to be fit for use in an application it is designed for. For example, if the characteristics of a medical implant used for controlled drug delivery are such that it releases the drug at a rate slower than an intended rate, then the purpose is not served. Typically, selection of the polymers required, blending ratio of the polymers, etc. is carried out through trial and error methods so as to meet the intended characteristics. Such trial and error approaches are time consuming and also are not cost effective due to the amount of expenses incurred in performing laboratory tests.
SUMMARY [004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in the conventional systems. For example, in

one embodiment, a processor implemented method for designing a biodegradable polymer based medical device is provided. In this method, initially at least one desired performance characteristic of the biodegradable polymer-based device is received as requirements from a user, via one or more hardware processors. Further, one or more candidate designs of the biodegradable polymer based medical device that match the requirements, from among a plurality of designs in a design specifications database, is selected. Further, one or more properties of the biodegradable polymer based medical device for each of the selected one or more candidate designs is predicted, using a mathematical model, via the one or more hardware processors. Further, performance characteristics of each of the one or more candidate designs of the biodegradable polymer based medical device are estimated, based on the predicted one or more properties. Further, the extent of deviation of performance characteristics of each of the one or more candidate designs of the biodegradable polymer based medical device in comparison with the desired performance characteristics of the biodegradable polymer based medical device is determined. One design from amongst the selected one or more candidate designs is recommended to a user, wherein the extent of deviation of the performance characteristics of the medical device with the recommended design is within a permissible secondary threshold of deviation and is minimum among the selected candidate designs if more than one candidate design is selected. If the extent of deviation of the performance characteristics of the one or more designs of the biodegradable polymer based medical device is outside the permissible secondary threshold of deviation, then a new candidate design of the biodegradable polymer based medical device is created, and this process is repeated until an acceptable candidate design is found.
[005] In another aspect, a system for designing a biodegradable polymer based medical device is provided. The system includes one or more hardware processors, one or more communication interfaces, and one or more memory storing a plurality of instructions. The plurality of instructions when executed cause the one or more hardware processors to receive at least one desired performance characteristic of the biodegradable polymer-based medical device, as requirements

from a user. Further, one or more candidate designs of the biodegradable polymer based medical device that match the requirements, from among a plurality of designs in a design specification database, is selected by the system. Further, one or more properties of the biodegradable polymer based medical device for each of the selected one or more candidate designs is predicted, using a mathematical model, by the system. Further, performance characteristics of each of the one or more candidate designs of the biodegradable polymer based medical device are estimated, based on the predicted one or more properties. Further, the extent of deviation of performance characteristics of each of the one or more candidate designs of the biodegradable polymer based medical device in comparison with the desired performance characteristics is determined. One design from amongst the selected one or more candidate designs is recommended to a user, wherein the extent of deviation of the performance characteristics of the medical device with the recommended design is within a permissible secondary threshold of deviation and is minimum among the selected candidate designs if more than one candidate design is selected. If the extent of deviation of the performance characteristics of the one or more candidate designs of the biodegradable polymer based medical device is outside the permissible secondary threshold of deviation, then the system creates a new design, and this process is repeated until an acceptable candidate design of the medical device is found
[006] In yet another aspect, a non-transitory computer readable medium for designing a biodegradable polymer based medical device is provided. The non-transitory computer readable medium initially receives at least one desired performance characteristic of the biodegradable polymer-based device as requirements from a user, via one or more hardware processors. Further, one or more candidate designs that match the requirements, from among a plurality of designs in a design specifications database, is selected by the non-transitory computer readable medium. Further, one or more properties of the medical device corresponding to each of the selected one or more candidate designs is predicted by non-transitory computer readable medium, using a mathematical model, via the one or more hardware processors. Further, performance characteristics of each of the

one or more candidate designs of the medical device are estimated, based on the predicted one or more properties, by the non-transitory computer readable medium. Further, the non-transitory computer readable medium determines the extent of deviation of performance characteristics of each of the one or more candidate designs of the medical device in comparison with the desired performance characteristics of the medical device. One design from amongst the selected one or more candidate designs is recommended to a user, wherein the extent of deviation of the performance characteristics of the medical device with the recommended design is within a permissible secondary threshold of deviation and is minimum among the selected candidate designs if more than one candidate design is selected. If the extent of deviation of the performance characteristics of the one or more designs is outside the permissible secondary threshold of deviation, then a new design is created, and this process is repeated until an acceptable candidate design is found.
[007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[009] FIG. 1 illustrates an exemplary system for designing a biodegradable polymer based medical device, according to some embodiments of the present disclosure.
[010] FIG. 2 is a functional block diagram depicting example implementation of the system of FIG. 1, according to some embodiments of the present disclosure.
[011] FIG. 3 is a block diagram depicting components of a prediction unit of the system in FIG. 2, in accordance with some embodiments of the present disclosure.

[012] FIG. 4 is a block diagram depicting components of an object estimation and analysis unit of the system in FIG. 2, in accordance with some embodiments of the present disclosure.
[013] FIGS. 5A and 5B illustrate a flow diagram depicting steps involved in the process of designing the biodegradable polymer based medical device, using the system of FIG. 1, in accordance with some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS [014] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.
[015] Referring now to the drawings, and more particularly to FIG. 1 through FIG. 5B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[016] FIG. 1 illustrates an exemplary system for designing a biodegradable polymer based medical device, according to some embodiments of the present disclosure. It is to be noted that the terms bio-degradable polymer based device/biodegradable polymer based device/medical device/device, are used interchangeably throughout the specification. The system 100 may be implemented in a computing device. Examples of the computing device include, but are not limited to, mainframe computers, workstations, personal computers, desktop computers, minicomputers, servers, multiprocessor systems, laptops, a cellular

communicating device, such as a personal digital assistant, a smart phone, and a mobile phone; and the like. The system 100, implemented using the computing device, includes one or more hardware processor(s) 102, IO interface(s) 104, and a memory 106 coupled to the processor 102. The processor 102 can be a single processing unit or a number of units. The hardware processor 102, the memory 106, and the IO interface 104 may be coupled by a system bus such as a system bus 112 or a similar mechanism. The processor 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 102 is configured to fetch and execute computer-readable instructions and data stored in the memory 106.
[017] Functions of the various elements shown in the figures, including any functional blocks labeled as “processor(s)”, may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or customized, may also be included.
[018] The IO interfaces 104 may include a variety of software and hardware interfaces, for example, interface for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. Further, the IO interfaces 104 may enable the computing device to communicate with other computing devices, such as a personal computer, a laptop, and like.

[019] The memory 106 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 106 may also include module(s) 108 and data 110.
[020] The modules 108 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types. The modules 108 may include programs or computer-readable instructions or coded instructions that supplement applications or functions performed by the system 100. The modules 108 may also be used as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the modules 108 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. In an embodiment, the modules 108 can include various sub-modules and other module(s) 116. The other module(s) 116 may include programs or coded instructions that supplement applications and functions of the computing device.
[021] The data 110, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the module(s) 108. The data 110 includes, for example, one or more specifications of each of the selected one or more designs of the device, properties of the constituents for e.g. polymer properties, properties of the drug and any other active molecules that are used in the formulation of the device, mathematical model, model parameters of the mathematical model, requirements received/collected from each user, recommendations generated for each requirements received, and so on, and other data. The other data includes data generated as a result of the execution of one or more modules in the other module(s).
[022] FIG. 2 is a functional block diagram depicting example implementation of the system of FIG. 1, according to some embodiments of the

present disclosure. The system of FIG. 1 can be implemented in a variety of ways. The data processing being carried out by the processors 102 can be distributed among the components depicted in FIG. 2. The system in FIG. 2 includes the I/O interface 104, the memory 106, a prediction unit 202, an objective estimation and analysis unit 204, a design evaluation unit 206, and a candidate design generation unit 208. The memory 106 includes a plurality of databases including a design specification database 210, a materials database 212, a model parameter database 214, and a physiological properties database 216. The design specifications database 210 contains information of polymer or blend of polymers used, blending ratio of different polymer used in the polymer blend, molecular weight of each polymer, and other polymer specific properties, that are used in the formulation of the biodegradable polymer based medical device. Further, the shape and dimensions of medical device, its physiological application are also stored in the design specifications database 210. The performance characteristics of the device such as lifespan, release rate of drug molecule (either measured or predicted) are also stored in the design specifications database 210. Additionally, information that aid the simulation such as computational geometry of the device, computational mesh, and so on also are stored in the design specifications database 210. An example structure of the design specifications database 210 is given in Table. 1. It is to be noted that the parameters listed in Table. 1 are for example purpose only, and can vary between different implementations as per requirements.

Design ID Polymers Blending ratio Molecular weight [g/mol] Physiological location applied Application as Shape
1 PLA,PCL [0.9,0.1] [9800, 200,000] Shoulder joint Screw Cuboid
2 PLGA [1.0] 44,000 Subcutaneous tissue Drug
delivery
device Spherical
3 … … … … … …

Table. 1 [023] The material database 212 stores data such as but not limited to properties of the different materials that can be used in device design/formulation. For e.g. for each polymer, data such as but not limited to molecular weight, polydispersity, glass transition temperature, and density are stored. Also, for each drug, for e.g. Insulin, properties such as but not limited to density, and molecular weight can be stored in the materials database 212. An example structure of the materials database 212 is given in Table. 2. It is to be noted that the parameters listed in Table. 2 are for example purpose only, and can vary between different implementations as per requirements.

Material Material type Mw (g/mol) Poly dispersity Tg (C) Density (g/cm3) Property name
PLGA Polymer 44,000 1.5 44 1.34 …
PCL Polymer 200,000 2.4 55 1.145 …
Table. 2 [024] The model parameter database 214 stores information such as but not limited to the parameters of the mathematical model for a given device design. This can include hydrolytic degradation rate constant, autocatalytic reaction rate constant, diffusion coefficient of different types of molecules such as drug, water, oligomers, and various ions that can transport in and out of the device and so on. An example structure of the model parameters database 214 is given in Table. 3. It is to be noted that the parameters listed in Table. 3 are for example purpose only, and can vary between different implementations as per requirements.

Diffusion Hydrolytic Autocatalytic
Material Material type coefficient (oligomer/active reaction rate constant reaction rate constant
molecule) m2/s (m3/mol-s) (m4.5/mol1.5-s)

PCL Polymer/polymer blend 1.16 x 10-17 3.75x10-14 2.1x10-13
PCL:PLGA 10:90 Polymer/Polymer blend 2.34 x 10-17 1.42x10-15 2.1x10-15
Insulin Active molecule 4.65 x 10-11 Not applicable Not applicable
Water Active molecule 1.2 x10-9 Not applicable Not applicable
.. .. .. .. ..
Table. 3 [025] The physiological properties database 216 contains data corresponding to different physiological locations where the device can be applied. Morphology in form of image, or actual dimensions of the location can be stored in a computer readable file format. Further, information related to environment properties such as but not limited to composition of the physiologic fluid in that location can also be provided in terms of pH, ionic strength, temperature, and different molecules that are present in the environment. An example structure of the physiological properties database 216 is given in Table. 4. It is to be noted that the parameters listed in Table. 4 are for example purpose only, and can vary between different implementations as per requirements.

Physiological Location 3D
Image
file Environment properties
Shoulder joint File1 pH Ionic strength Temperature Composition

7.35 50 mM 37oC [Hyaluronic
acid,
phospholipid,
proteoglycan]

Subcutaneous tissue File3 7.4 NA 37oC NaCl, KCl, Na2HPO4,
H2PO4
… … . . ... … ...
Table. 4
[026] The prediction unit 202 includes a device degradation prediction unit 302, a structural properties prediction unit 304, and an active molecule transport prediction unit 306 (as depicted in FIG. 3). The objective estimation and analysis unit 204 includes a post processing unit 402, and an objective computation unit 404 (as depicted in FIG. 4). Various steps executed by the system 100 are depicted in FIG. 5, and are now being explained with reference to the hardware components depicted in FIG. 1 through FIG. 4.
[027] The system 100 receives/collects (502) desired performance characteristics, physiological application of a biodegradable polymer based medical device (alternately referred to as ‘device’) to be designed, as inputs. In addition, the system 100 also collects environmental properties at location(s) where the biodegradable polymer based medical device are to be used. The “desired performance characteristics” of the device represent intended characteristics in terms of parameters such as but not limited to of lifespan of device, release rate of encapsulated drug, and value of a range of mechanical properties such as but not limited to tensile strength, and compressive strength, the medical device need to have so as to be suitable to perform the intended physiological application. Some examples of the physiological application are devices being used as bio-implants, scaffolds for tissue regeneration, artificial bones, and as a carrier to enable controlled or targeted release of drug. Some examples of the environmental properties are, but not limited to, temperature, and pressure at the location where the biodegradable polymer based medical device is placed.
[028] The system 100 maintains in the memory 106, a design specifications database 210 that stores information for multiple designs of the

devices that may be reused, information for one or more performance characteristics, which are either measured in the laboratory or predicted by the system 100, of each of design. The system 100 compares the desired performance characteristics with the corresponding performance characteristics of each of the designs in the design specifications database 210. Based on the comparison, the system 100 selects (504) one or more of the designs as candidate designs, wherein extent of the deviation between the desired performance characteristics and the characteristics of the candidate design is below a primary threshold. The extent of deviation can be calculated using an error measure such as mean squared error, absolute error etc. depending on the type of performance characteristics that are compared. Value of the primary threshold may be pre-defined or may be dynamically defined as per requirements, by an authorized user, using an appropriate interface provided by the I/O interface 104. In an embodiment, the design specification database 210 may not have information on all the performance characteristics specified as the desired performance characteristics, for one or more of the candidate designs. For any of the candidate designs, if the system 100 identifies that one or more of the performance characteristics is not defined/available in the design specifications database 210 (the performance characteristics that are not available/defined are also termed as ‘missing performance characteristics’), at step 506, a plurality of properties of the medical device that can be either time evolving or point estimates, are predicted using prediction unit 202 of the system 100. The prediction unit 202 receives inputs of device specifications from design specifications database 210 and model parameters corresponding to the constituents present in the device from the model parameters database 214 and uses these inputs to perform simulations using a mathematical model that predicts the properties of the candidate design of a medical device. In particular, a candidate design is specified in terms of: a) polymers used, b) molecular weight of each of the polymers used, c) density of each of the polymers, d) blending ratios of each of the polymers used in the formulation, e) shape of the biodegradable polymer based medical device, and f) dimensions of the biodegradable polymer based medical device. These device specifications are used

to estimate device degradation properties, structural properties of the device, and release or uptake kinetics of active molecules from the device at the intended physiological location. The degradation properties estimated by the prediction unit include molecular weight of one or more of the blend of polymers and total mass of the device, while structural properties include crystallinity, tensile strength, and compressive strength of one or the blended polymers, whereas transport properties include cumulative release of any encapsulated drug or active molecule with time from the biodegradable polymer based medical device. These predicted properties are post-processed to calculate the performance characteristics of the medical device. The prediction unit 202 has 3 sub-units: a) a device degradation prediction unit 302 that predicts one or more of degradation characteristics of the device, b) a structural properties prediction unit 304 that predicts one or more structural properties of the device, and c) an active molecule transport prediction unit 306 that predicts active molecule transport characteristics of the device. These sub-units are depicted in FIG. 3. Mathematical models implemented in these sub-units that are used to estimate various properties of a medical device is given below:
Mathematical Model:
[029] The mathematical model implemented in the three sub-units of the prediction unit 202 is configured to predict the changing molecular weight of the polymer or the blend of polymers that makes up the device, mass loss from the device, and release rate of the drug or active molecules from the medical device. When the medical device is placed inside a human body, water and other active molecules diffuse from the surrounding medium into the biodegradable polymer based device and react with the ester linkages of the polymer, resulting in chain scission and thereby formation of new and smaller polymer chains. Chain scission also results in formation of oligomers, chains with four to eight monomer repeating units. These oligomers contain carboxylic acids as the end groups which further increase the rate of reaction due to autocatalytic degradation. The smaller polymer chains may come together and form new crystallites thus leading to crystallization. The amorphous phase tends to degrade faster than the crystalline phase. Model

equations implemented in each of the three sub-units are described in the following
sections:
By device degradation prediction unit (302):
1. Chain scission
[030] The rate of chain scission influences the degradation of the polymer and also results in mass loss from the device due to diffusion of the smaller polymer chains and oligomers. The rate of reaction proceeds with different rates in the
amorphous and the crystalline phase The
expressions for both can be written as follows and are implemented in the device degradation prediction unit 302:

where, Ram, represents number of chain scissions in the amorphous phase and Rcrys represents total number of chain scissions taking place in the crystalline phase. k1am and k1crys are reaction rate constants due to non-catalytic hydrolysis (acid groups not participating). k2am and k2crys represent rate constants for autocatalytic degradation in amorphous and crystalline phase respectively. As a result of chain scission, both smaller polymer chains and oligomers (with four to eight repeating units) will be formed. Concentration of ester groups in the oligomers formed due to chain scission can be estimated by the following equation introduced by Han and co-workers (Han et al., Acta Biomaer, 2010, 3882-3889):

Here, Rol, represents concentration of ester groups in the oligomers formed, Ce0, is initial concentration of ester groups in the polymer chains. Rs is total number of chain scissions at any time t, given by Ram + Rcrys.
[031] The oligomers are more mobile than polymer chains and the carboxylic acid end group present on them participates in the catalytic reactions. The concentration of carboxylic acid groups is estimated by molar concentration of

chains of oligomers, Col, divided by the average degree of polymerization of oligomers, m.
[032] Number degree of polymerization, Ndp, is estimated by deducting the concentration of ester linkages lost due to formation of oligomers and crystallization from the initial concentration of ester groups in polymers.

[033] Here ω0 is initial concentration of ester groups in the crystalline phase estimated using is initial degree of
crystallization of the polymer. Nchain is total number of polymer chains present in the device while accounting for chain scission.
[034] Once the number degree of polymerization, Ndp, is estimated, molecular weight of polymer at any time t is given by, Mn(t) = Ndp Munit,where, Munit, is molecular weight of the monomer, repeating unit of the polymer.
2. Mass loss of the device
[035] Polymer degradation leads to the formation of oligomers and smaller polymer chains that can diffuse through the device resulting in the loss of mass of the device. Device degradation prediction unit 302 accounts for this mass loss by including appropriate equations in the model. The concentration of the chains of oligomers,Col, in the device can be estimated as follows.

[036] Rol is rate of oligomer formation due to random chain scission as explained previously and Dol is diffusion coefficient of the oligomers in the device.
[037] In some cases, polymer chains, especially those with significantly lower degree of polymerization, can also diffuse out. The spatial distribution of chains across the device length can be estimated as shown in Equation (7), which is implemented in the device degradation prediction unit 302.


Here, Rs, is the rate of chain scission that is summed over for both amorphous and crystalline phases of the polymer. DNchain is diffusion coefficient of the polymer chains, which greatly depends on the length of polymer chains, decreasing almost significantly as the length of polymer chain increases. The mass loss from the device is due to the diffusion of smaller polymer chains and
oligomers. The fractional mass loss can estimated as where,
is the initial concentration of ester groups that are present in the polymer, and from it are deducted the ester groups concentration that are present in the oligomers (Col), and in polymer chains NchainNdp at any time t once degradation starts.
By structural properties prediction unit (304):
1. Crystallization of the amorphous phase
[038] During chain cleavage/scission due to hydrolysis, new chains formed act as nucleation sites for further crystallization. Change in crystallinity of polymer is estimated using the equations given below which are implemented in the structural properties prediction unit 304:

[039] Where, Xc is degree of crystallization and Xext is extended degree of crystallization. p is probability of crystallite formation, vc is volume of a crystallite and Ram is number of chain scissions in the amorphous phase of the polymer.
[040] Although this equation only provides prediction of crystallinity of the polymer, mechanical properties such as compressive strength and tensile strength can be also be calculated by including other appropriate equations such as force balance equation. By active molecule transport prediction unit (306):
1. Diffusion of water

[041] Water is one of the most prominent components in the physiological fluids. It also plays an important role in the degradation of polyester based polymers as explained previously. In absence of any additional affinity towards the device, the concentration difference is the major contributor to the transport of water through the device. Further, water concentration inside the device is affected by hydrolytic degradation as shown by the Equation (10), which is implemented in the active molecule transport prediction unit 306.

where, Cw, is concentration of water inside the device. Dw is diffusion coefficient of water in the device. Ram is rate of hydrolysis reaction in the amorphous phase, and Rcrys is rate of hydrolysis reaction in the crystalline phase.
2. Drug release
[042] Depending on the nature of drug, hydrophilic or hydrophobic, active molecule transport prediction unit 306 refers to the applicable set of equations to predict the concentration of drug in the device. Diffusion is a rate limiting step for the transport of a hydrophilic drug from the device to the surrounding medium as expressed in Equation 11.

Where, CDr, is concentration of drug and, DDr, is diffusion coefficient of drug in the device. It is assumed that drug instantaneously dissolves in the water present in the porous network.
[043] However, in case of a hydrophobic drug, its dissolution in water present in the porous structure of the medical device can be rate limiting. In that case, the following two equations are needed to predict drug release from the device.


Where, CDS r, is concentration of the drug in its non-soluble form, and kdis is dissolution rate constant of the drug.
[044] The diffusion coefficient of the species (water, drug, oligomers) is estimated using the following equation

Here i refers to species that diffuses into and out of the device. The diffusivity of a molecule in the pore is estimated using the Polson correlation, presented below.

[045] The equations in the mathematical model as described above are coupled. Thus, each of the sub-units of the prediction unit 202 communicate with each other to transmit the relevant information that facilitates prediction of change in molecular weight of polymer, mass loss of the device, change in crystallinity of the polymer, and release kinetics of the drug or any active molecule. It should also be noted that the equations above are applicable for a shape of the device where change along one of the dimensions is rate controlling. For more complex shapes, the equations implemented in each of the sub-units can be extended to include contributions along other directions too.
[046] Further at step 508, the post processing unit 402, a component of the objective estimation and analysis unit 204, estimates the performance characteristics of each of the one or more candidate designs, using the predictions of degradation properties, structural properties, and drug release kinetics received from the Prediction unit 202. For example, time evolution of mass of the device as predicted from the prediction unit 202, is used to estimate lifespan of the device. A pre-defined percentage of mass loss, say 60%, can be used as a threshold to represent a state of device that has degraded beyond use. The time spent in reaching the threshold percentage of mass loss is used to estimate lifespan or remaining useful life of the device. On the other hand, average release rate of drug or active molecule from the device is estimated using the predicted data for concentration profile of the drug in the device. The concentration gradient at the boundary of the device is approximated using a suitable numerical differentiation technique and

multiplied by diffusion coefficient of the drug and surface area of the device to arrive at the average drug release rate.
[047] For a candidate design, the predicted values of one or more of the performance characteristics and the values available in the design specifications database 210 for rest of the performance characteristics, the latter applicable if the candidate design is already present in the design specifications database 210, together represent the performance characteristics of that particular design. The performance characteristics of each of the candidate designs is then compared with the desired performance characteristics, by the objective computation unit 404, a component of objective estimation and analysis unit 204. By virtue of the comparison, the objective estimation and analysis unit 204 determines (510) an extent of deviation of the performance characteristics of each of the designs in comparison with the desired device performance characteristics. The extent of deviation is calculated using an appropriate error measure depending on the nature of performance characteristics. For example, absolute error between the predicted and desired lifespan of the device can be used as an extent of deviation. On the other hand, mean absolute error, may be used when comparing predicted and desired drug release profile over a given time period. In an embodiment, the extent of deviation is determined separately for each of the performance characteristics. In another embodiment, the extent of deviation is determined and represented at a design level by weighted summation of deviation in each of performance characteristics.
[048] Further at step 512, the design evaluation unit 206 compares the determined extent of deviation of each of the candidate designs with a secondary threshold of deviation. If the extent of deviation is less than the secondary threshold for a candidate design, then the candidate design is recommended to the user at step 514. If, for multiple candidate designs, the extent of deviation is less than the secondary threshold of deviation, then the design evaluation unit 206 may compare the extent of deviation determined for each of the candidate designs with one another, and may recommend the candidate design having the least value of extent of deviation from amongst the candidate designs determined as having the extent of deviation less than the secondary threshold of deviation. If only one candidate

design is determined as having the extent of deviation less than the secondary threshold of deviation, the same is recommended to the user. In another embodiment, if only one design is selected as the candidate design at step 504, and if the extent of deviation for the candidate design is less than the secondary threshold, the candidate design is recommended to the user.
[049] However, if at step 512 the design evaluation unit 206 determines that the extent of deviation exceeds the secondary threshold of deviation, then the candidate design generation unit 208 of the system 100 creates (516) a new candidate design via a multi-step optimization procedure, wherein the multi-step optimization procedure involves changing at least one of a) dimensions of the device within the physiological limits, b) shape of the device within physiological limits, c) blending ratio of each of the polymers used in the formulation if more than one polymer is used, and d) polymer used in the formulation or its properties, to match the desired performance characteristics. Candidate design generation unit 208 consists of a library of optimization solvers that are called to arrive at a new candidate design given the current computed value of the objective as returned by objective estimation and analysis unit 204 and the corresponding design constraints. The extent of deviation of performance characteristics as returned by the objective estimation and analysis unit 204 is used to formulate an objective function, while physiological constraints on the shape and the dimensions are used as constraints. The multi-step optimization procedure changes one decision variable at a time, i.e. one amongst dimension, shape, blending ratio, and polymer itself, to generate a new candidate design. Initially, only the dimensions of the device are used as decision variables. The properties of the candidate designs are predicted by the prediction unit 202 for a new candidate design of the device generated by candidate design generation unit 208, followed by the estimation of performance characteristics and computation of the extent of deviation by objective estimation and analysis unit 204. In case new designs suggested based on dimensions alone are not found to be acceptable by the design evaluation unit 206, then at the next step of multi-step optimization, the shape of the device itself is changed. Different shapes available in the design specifications database 210 are used as reference set and are selected and

evaluated one at a time, in case the shape of the device along with its dimensions satisfy the physiological constraints. The procedure is repeated to suggest new candidate designs with different blending ratios of the polymers, followed by changing the polymers or the properties of the polymers themselves used in the formulation, if changing the shape of the device too does not provide acceptable performance. While changing polymers or their properties used in the formulation of the device, one or more combinations of polymers or their properties as present in the materials database 212 can be used. The properties of the polymer refer to its molecular weight, density, polydispersity, initial crystallinity etc. The candidate design generation unit 208 adapts optimization algorithm used to generate the candidate design depending on the nature of the decision variable to be changed. For example, in case of changing the dimensions of the device, where dimensions represent one or more real numbers, methods such as genetic algorithm, sequential quadratic programming, etc. can be used. On the other hand, for changing the polymer or combination of polymers, a discrete variable, techniques such as complete enumeration or variants of genetic algorithm, particle swarm optimization that support handling of discrete variables can be used. Candidate design generation unit 208 keeps on generating new feasible designs till acceptable performance criteria are met.
[050] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[051] The embodiments of present disclosure herein addresses the unresolved problem of design of biodegradable polymer based medical devices. The embodiment, thus provides a method and system for recommending one or more designs that match user requirements.

[052] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[053] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[054] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are

appropriately performed. Alternatives (including equivalents, extensions,
variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[055] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[056] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor implemented method (500) for designing a bio-degradable polymer based medical device, comprising:
receiving (502) at least one desired performance characteristic, and physiological application, of a biodegradable polymer based medical device as requirements, via one or more hardware processors; selecting (504) one or more candidate designs of the bio-degradable polymer based medical device that match the requirements, from amongst a plurality of designs in a design specifications database, via the one or more hardware processors;
predicting (506) one or more properties of the bio-degradable polymer based medical device for each of the selected one or more candidate designs of the bio-degradable polymer based medical device, using a mathematical model, via the one or more hardware processors; estimating (508) performance characteristics of each of the one or more candidate designs of the bio-degradable polymer based medical device, based on the predicted one or more properties, via the one or more hardware processors;
determining (510) an extent of deviation of the estimated performance characteristics of each of the one or more candidate designs of the bio-degradable polymer based medical device in comparison with the at least one desired performance characteristics of the bio-degradable polymer based medical device, via the one or more hardware processors; and
generating a recommendation, via the one or more hardware processors, wherein generating the recommendation comprising performing one of:
recommending (514) one design from amongst the selected one or more candidate designs, to a user, wherein the extent of deviation of the performance characteristics of the bio-degradable polymer

based medical device with the recommended design is within a permissible secondary threshold of deviation and is minimum among the selected designs if more than one design is selected; and
creating (516) a new candidate design of the bio-degradable polymer based medical device through multi-step optimization if the extent of deviation of the performance characteristics of the one or more designs of the medical device is outside the permissible secondary threshold of deviation.
2. The processor implemented method (500) as claimed in claim 1, wherein the one or more performance characteristics of the bio-degradable polymer based medical device comprises at least one of lifespan of the bio¬degradable polymer based medical device, release rate of encapsulated drug or active molecule, and value of range of mechanical properties of the bio¬degradable polymer based medical device, further wherein the mechanical properties of the bio-degradable polymer based medical device comprise one or more of tensile strength, and compressive strength.
3. The processor implemented method (500) as claimed in claim 1, wherein the mathematical model comprises sub-models for predicting degradation properties of the bio-degradable polymer based medical device, transport properties of drug and active molecules, and one or more structural properties of the bio-degradable polymer based medical device.
4. The processor implemented method (500) as claimed in claim 1, wherein the mathematical model comprises one or more expressions to estimate at least one of a rate of polymeric chain scission, transport of water into the bio-degradable polymer based medical device, transport of drug and other active molecules from the bio-degradable polymer based medical device, rate of formation of oligomers and polymeric chains, rate of transformation

of amorphous phase of the different polymers to crystalline phase, and transport of oligomers from the bio-degradable polymer based medical device.
5. The processor implemented method (500) as claimed in claim 1, wherein the physiological application of the bio-degradable polymer based medical device is defined in terms of location inside a patient’s body and the corresponding environmental properties where the biodegradable polymer based medical device is to be placed, and an intended primary use, further wherein the intended primary use comprises one of bio-implant, scaffold for tissue regeneration, artificial bone, and a carrier for controlled drug delivery.
6. The processor implemented method (500) as claimed in claim 1, wherein the design of the bio-degradable polymer based medical device is specified in terms of at least one of a) polymers used, b) molecular weight of each of the polymers used, c) density of each of the polymers, d) blending ratios of each of the polymers used in the formulation of the biodegradable polymer based medical device, (e) shape of the biodegradable polymer based medical device, and (f) dimensions of the biodegradable polymer based medical device.
7. The processor implemented method (500) as claimed in claim 1, wherein the new candidate design of the bio-degradable polymer based medical device is generated by a multi-step optimization procedure, wherein the multi-step optimization procedure involves changing at least one of a) dimensions of the bio-degradable polymer based medical device within the physiological limits, b) shape of the bio-degradable polymer based medical device within the physiological limits, c) blending ratio of each of the polymers used in the formulation of the bio-degradable polymer based medical device, if more than one polymer is used, and d) polymer used in the formulation of the bio-degradable polymer based medical device and

properties of the polymer, to match the desired performance characteristics of the bio-degradable polymer based medical device. 8. A system (100) for designing a bio-degradable polymer based medical device, comprising:
one or more hardware processors (102);
one or more communication interfaces (104); and
one or more memory (106) storing a plurality of instructions, wherein
the plurality of instructions when executed cause the one or more
hardware processors to:
receive (502) at least one desired performance characteristic, and physiological application, of the biodegradable polymer based medical device as requirements;
select (504) one or more candidate designs of the bio-degradable polymer based medical device that match the requirements, from amongst a plurality of designs in a design specifications database; predict (506) one or more properties of the bio-degradable polymer based medical device for each of the selected one or more candidate designs of the bio-degradable polymer based medical device, using a mathematical model;
estimate (508) performance characteristics of each of the one or more candidate designs of the bio-degradable polymer based medical device, based on the predicted one or more properties; determine (510) an extent of deviation of the estimated performance characteristics of each of the one or more candidate designs of the bio-degradable polymer based medical device in comparison with the at least one desired performance characteristic of the bio-degradable polymer based medical device; and generate a recommendation, comprising performing one of:
recommending (514) one design from amongst the selected one or more candidate designs, to a user, wherein the extent

of deviation of the performance characteristics of the bio-degradable polymer based medical device obtained using the recommended design is within a permissible secondary threshold of deviation and is minimum among the selected candidate designs if more than one candidate designs is selected; and
creating (516) a new candidate design of the bio-degradable polymer based medical device through multi-step optimization if the extent of deviation of the performance characteristics of one or more candidate designs of the bio-degradable polymer based medical device is outside the permissible secondary threshold of deviation.
9. The system (100) as claimed in claim 8, wherein the one or more performance characteristic of the bio-degradable polymer based medical device comprises at least one of lifespan of the bio-degradable polymer based medical device, release rate of encapsulated drug or active molecule, and value of range of mechanical properties of the bio-degradable polymer based medical device, further wherein the mechanical properties of the bio-degradable polymer based medical device comprise one or more of tensile strength, and compressive strength.
10. The system (100) as claimed in claim 8, wherein the mathematical model comprises sub-models for predicting degradation properties of the bio-degradable polymer based medical device, transport properties of drug and active molecules, if any, and one or more structural properties of the bio-degradable polymer based medical device.
11. The system (100) as claimed in claim 8, wherein the mathematical model comprises one or more expressions to estimate at least one of a rate of polymeric chain scission, transport of water into the biodegradable polymer

based medical device, transport of drug and other active molecules from the biodegradable polymer based medical device, rate of formation of oligomers and polymeric chains, rate of transformation of amorphous phase of the different polymers to the crystalline phase, and transport of oligomers from the bio-degradable polymer based medical device.
12. The system (100) as claimed in claim 8, wherein the physiological application of the bio-degradable polymer based medical device is defined in terms of location inside a patient’s body and the corresponding environmental properties where the bio-degradable polymer based medical device is to be placed, and an intended primary use, further wherein the intended primary use comprises one of bio-implant, scaffold for tissue regeneration, artificial bone, and a carrier for controlled drug delivery.
13. The system (100) as claimed in claim 8, wherein the design of the bio-degradable polymer based medical device is specified in terms of at least one of a) polymers used, b) molecular weight of each of the polymers used, c) density of each of the polymers, d) blending ratio of each of the polymers used in the formulation of the biodegradable polymer based medical device, (e) shape of the biodegradable polymer based medical device, and (f) dimensions of the biodegradable polymer based medical device.
14. The system (100) as claimed in claim 8, wherein the system (100) generates the new candidate design of the bio-degradable polymer based medical device using a multi-step optimization procedure, wherein the multi-step optimization procedure involves changing at least one of a) dimensions of the bio-degradable polymer based medical device within the physiological limits, b) shape of the bio-degradable polymer based medical device within the physiological limits, c) blending ratio of each of the polymers used in the formulation of the bio-degradable polymer based medical device, if more than one polymer is used, and d) polymer used in the formulation of

the bio-degradable polymer based medical device and properties of the polymer, to match the desired performance characteristics of the bio-degradable polymer based medical device.