FIELD OF THE INVENTION

The present invention relates generally to lab processes. In particular, the present invention relates to a method and system for predicting outcomes of lab processes through real time monitoring of the processes and artificial intelligence techniques.

BACKGROUND OF THE INVENTION

Rising costs and declining approval rates are driving pharmaceutical and biotechnology companies to rethink on how they bring drugs to market. The approvals for New Molecular Entities (NMEs) are at a downward trend over the last few years despite an increase in the research and development spending. A substantial portion of this research and development spending goes for the lab operations which includes conducting various lab processes. However, these lab operations face serious issues in efficiency and productivity and that has resulted in the spiralling costs and NME drought. The main reason behind inefficient and less productive lab operations is the inability to monitor the heavily funded lab processes in real time and also to predict their outcomes. This inability can be attributed to a disparate Information and Technology (IT) application landscape in the labs, lack of real-time data of processes under execution, siloed data sets associated with the lab operations, challenges in extracting desired data from underlying legacy systems and instruments, and varied data formats that makes the monitoring of the processes and analysis of data for predicting outcomes an inefficient and expensive affair.

While there are solutions in the market that try to address one or more issues related to inability to monitor the lab processes in real time and to predict their outcomes, each of these solutions suffer from one or more drawbacks. Such drawbacks include inability to provide a connected lab environment that accommodates legacy lab systems and instruments, lack of standard data connections, and analytical capabilities without scalability.

In light of the above, there is a need for a solution that can use digital technologies to extract relevant data from underlying legacy lab systems and instruments, and other multiple sources in real-time. There is further a need for a solution that can allow monitoring of processes in real time. There is further a need for a solution that can efficiently analyse the extracted data to predict outcomes of processes performed in the labs.

SUMMARY OF THE INVENTION

In an embodiment of the present invention, a system for monitoring one or more lab processes and predicting their outcomes is provided. The system comprises a data acquisition module, a process setup and monitoring module, and an experiment prediction module. The data acquisition module is configured to acquire at least one of ambient data and experimental data in real time from one or more lab resources and instruments. The one or more lab resources and instruments comprise Internet of Things (IoT) enabled lab instruments, computer connected lab instruments, lab and enterprise systems, time series database systems, existing Application Programming Interfaces (APIs), content libraries, voice inputs from one or more lab technicians and investigators working in the lab, and data historians. In an embodiment of the present invention, the voice inputs from one or more lab technicians and investigators are acquired using an artificial intelligence based virtual lab assistant. The virtual lab assistant is further configured to guide the one or more technicians to follow a predefined protocol adopted by the lab for executing a process. Further, the virtual lab assistant applies employs a cognitive agent to perceive and act upon voice inputs.

The process setup and monitoring module is configured to receive the acquired data and facilitate setting-up and monitoring of one or more processes in real time utilizing the received data. The process setup and monitoring module is configured to setup the one or more processes using one or more recipes created by a recipe builder tool. The recipe builder tool is configured to create software codes associated with the one or more recipes with every user interaction. In an embodiment of the present invention, the recipe builder tool has customizable prebuilt templates of unit operations and parameters associated with the one or more recipes to setup the one or more recipes. The customization comprises editing properties of the prebuilt templates to make them compliant to one or more business requirements. Further, the one or more created recipes facilitate creation of one or more campaigns for one or more drug candidates, wherein campaign of the one or more campaigns have one or more lots. The one or more lots are monitored for variation in range values of the one or more parameters associated with the one or more processes. In an embodiment of the present invention, when status of a parameter of the one or more parameters varies from a predefined range of values alerts are triggered by the process setup and monitoring module.

In an embodiment of the present invention, the system further comprises a resource tracking module configured to obtain the data associated with the one or more lab resources and instruments and facilitate tracking, mapping and planning of the one or more lab resources and instruments using the obtained data, the resource tracking module is further configured to generate one or more reports on usage and efficiency of the one or more lab resources and instruments. The experiment prediction module configured to obtain data from the data acquisition module and process setup and monitoring module. The experiment prediction module is further configured to employ one or more machine learning techniques to generate one or more patterns to predict outcomes of the one or more processes conducted in the lab in real time. In an embodiment of the present invention, the system furthermore comprises a content library communicatively coupled to the experiment prediction module and configured to provide access to one or more internal and external content providers and tools to facilitate prediction of outcomes of lab processes.

In another embodiment of the present invention, a method for monitoring one or more lab processes and predicting their outcomes is provided. The method comprises acquiring at least one of ambient data and experimental data in real time from one or more lab resources and instruments. The one or more lab resources and instruments comprise Internet of Things (IoT) enabled lab instruments, computer connected lab instruments, lab and enterprise systems, time series database systems, existing Application Programming Interfaces (APIs), content libraries, voice inputs from one or more lab technicians and investigators working in the lab, and data historians.

The method further comprises setting up one or more processes in real time utilizing the acquired data, wherein the one or more recipes are set-up using one or more recipes generated by a recipe builder tool. The recipe builder tool is configured to create software codes associated with the one or more recipes with every user interaction. In an embodiment of the present invention, the recipe builder tool has customizable prebuilt templates of unit operations and parameters associated with the one or more recipes to setup the one or more recipes, further wherein the customization comprises editing properties of the prebuilt templates to make them compliant to one or more business requirements. Further, the one or more created recipes facilitate creation of one or more campaigns for one or more drug candidates, wherein campaign of the one or more campaigns have one or more lots. The one or more lots are monitored for variation in range values of the one or more parameters associated with the one or more processes. In an embodiment of the present invention, alerts are triggered when status of a parameter of the one or more parameters varies from a predefined range of values.

In yet another embodiment of the present invention, a non-transitory computer-readable medium having computer-readable program code stored thereon is provided. The computer-readable program code comprising instructions that when executed by a processor, cause the processor to acquire at least one of ambient data and experimental data in real time from one or more lab resources and instruments. The processor is further caused to setup up one or more processes in real time utilizing the acquired data, wherein the one or more recipes are set-up using one or more recipes generated by a recipe builder tool. The processor is furthermore caused to monitor the one or more processes in real time and predict outcomes of the one or more processes. In an embodiment of the present invention, one or more machine learning techniques are employed on the acquired data to generate one or more patterns to predict outcomes of the one or more processes conducted in the lab in real time.

