Computational framework to identify biomarkers using omics data constitutes a major part of the drug discovery, development and therapeutics. Identification of diagnostic, predictive. While diagnostic, prognostic, predictive, pharmacodynamic/ drug response, susceptibility/ risk and safety biomarkers can be identified successfully using Innoplexus biomarker platform, RNAseq, HTS/ NGS, microarray, WES, proteomics and scRNAseq datasets can be used to identify genes, proteins or non-coding RNAs as potential biomarkers. Publicly available datasets from databases such as GEO Omnibus, ArrayExpress and PRIDE, etc, as well as client datasets can be integrated into biomarker pipelines. Beyond the analysis of surrogate endpoint or clinical endpoint biomarker analysis, Innoplexus platform provides deep-dive analysis into transcriptional regulation and cell specific differential expression.

Gene regulatory network analysis provides insights into transcription factors and their target interplay, also aids in understanding the hierarchy of transcription factors in a given network along with identification of master regulators. scRNAseq analysis platform enables investigations into distinct cell populations,their relative proportions and cell type specific differential expression of genes across different experimental conditions.

Discovery or designing of molecules with desired properties (for example: a molecule that binds to a protein of interest), is novel (i.e. no patent exists for this molecule) and satisfies drug-like properties such as solubility, bioavailability, non toxicity) is an effort intensive and time-consuming task. Moreover, even with such high efforts and time investment, the rate of success in generatoing molecules the area of getting desirable molecules that succeed in a drug discovery pipeline is very limited.

At Innoplexus, we use an AI driven approach for generation of optimized molecular structures to facilitate drug discovery endeavor in the following ways:

• Generation of molecular structures that can serve as new ideas/hypotheses towards modulating a drug target.
• Optimization of molecular structures based on pre-existing molecules for a drug target.
• Obtain new molecular structures with acceptable properties for moving into the drug discovery pipeline faster.
• Our Deep-generative models leverage the existing molecular and biological data for training, along with cross-validation using scientific literature to provide valuable insights. In addition, we have also developed a molecule prioritization pipeline to evaluate the generated molecules based on: 1) Target protein binding potential, 2) Drug-like properties such as Lipinski’s rules, Quantitative Estimate of Drug-likeness (QED) and Synthetic Accessibility (SA), 3) ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties.

Target identification is one of the most crucial steps in drug discovery, almost half of the clinical trials failed due to in-efficacy of selected drugs. One of the reasons for this is inadequate target validation. Therefore, it is very important to understand and explore the role of all the potential molecules involved in the disease pathophysiology.

Innoplexus leverages its AI based discovery engine to identify the most promising target with high biological relevance and market potential. The advantage of AI-based process are: an automation of manual and labor intensive tasks, an ability to process, integrate and make sense of the large volumes of complex and unstructured datasets and an AI-enhanced process of knowledge discovery for understanding their cross-connections in biological network, which is especially important in the early phases of drug discovery. AI can accelerate the target identification process and also it can optimize the identification and optimization of lead molecule discovery. AI does so by searching through the past knowledge of the compounds, by examining a large variety of combinations and by recommending the most suitable leads. In particular, deep learning methods can explore existing data to help predict how tissue or body systems may respond to a given drug.

Further, AI can play a role in the identification of target patient populations for clinical trials, in predicting disease progression based on molecular data obtained from tissue samples and in stratifying patients either in the clinical trials preparation phase or in the optimization of the treatments based on patients’ responses and their individual characteristics.

Traditional method of drug design and discovery is a time consuming, costly and laborious process. It takes ~12-15 years for a drug to reach the market and costs more than $1 billion. Identification of valid lead molecules is a key step in drug discovery and is a tedious process along with a high failure rate. Several drugs are discovered through the traditional approaches, however only a few of them reach the clinic. Most of them fail in the preclinical and clinical stage due to safety and efficacy issues. Hence, there is a huge need for an AI based model which can predict bioactivity faster and in advance, so that a lot of money, effort and time can be saved by the pharma companies in addition to improving treatment outcomes for the patients.
Prediction of a potential drug’s bioactivity and ADMET properties with high accuracy not only reduces the cost of the drug discovery process but also has a direct positive impact on an individual’s health. For example, if we estimate the bioactivity of a potential drug well in advance, we can say whether it can be selected for further studies or not, thereby saving money, time and efforts.
Innoplexus has thus come up with a solution that leverages Cutting-Edge Technology to empower decision making. This involves development of an advanced SAR model consisting of transformer architecture ensembled with a deep learning (DL) based prediction model (state-of-the-art-method) to predict bioactivity of molecules. Also, PaDEL based descriptor models using traditional machine learning (ML) methods for bioactivity prediction have been developed, to enable the users to view and compare both ML and DL models, and thus select the best performing one based on the evaluation metrics.

Discovering novel uses for existing drugs through drug repurposing can cut down the time and costs, and at the same time reduce the risk of failure associated with the development of new therapeutics. At Innoplexus we harness our AI/machine learning capabilities and corporate assets to identify and validate drug repurposing candidates at an unprecedented scale and depth in a modality-independent fashion. We integrate all relevant data from publicly available as well as proprietary resources to comprehensively assess approved drugs for their therapeutic utility and market potential in other indications. Repurposing existing drugs can decrease the cost and shorten drug development processes because many of the preclinical and clinical safety studies have already been completed before and after market approval. Drug repurposing can also improve the success rate of drug development because marketed drugs often have well-characterized safety and efficacy profiles across geographies and stratified patient cohorts

Such advantages are honored by regulatory authorities with shortened and expedited drug approval processes. At Innoplexus we tighten up the data-driven safety even further by validating drug repurposing candidates with AI-tailored predictive biomarkers to track the efficacy of repurposed therapeutics and with our AI-powered clinical trial prediction tool.

Pharma companies spend more than 1 Billion USD on developing a new drug. Drug candidates pass the clinical stage with a success rate as low as 10-15%. Of all clinical studies, 86% fail to meet recruitment targets on time. Dropout rates of clinical trials commonly range between 15-40%. Of failed trials, 57% show limited efficacy i.e. due to poor statistical endpoints or underpowered samples and thus making clinical trials inherently more time consuming and expensive.

Predicting the probability or likelihood of success (PoS) for a clinical trial is important for pharma investors and to the clinical investigators that help them in evaluating the economic and scientific decisions. Without the proper estimate of PoS there is a chance for misjudging the risk and value proposition for the drug development that may lead to loss of opportunities for both investors and patients. Furthermore, to help prioritize the proper resource allocations, accurate and timely assessment of risk is critical and is the need of the hour for the drug development companies conducting the clinical trials.

Innoplexus, an AI-based company, leveraged its life sciences data ocean and proprietary AI technologies to build a clinical trial prediction (CTP) engine that can predict the probability of success for a clinical trial.

Identifying and prioritizing potential indications for selected targets is critical for clinical and commercial success. Given a target or drug, we leverage our proprietary algorithms and AI based technologies to identify most promising indications which are biologically significant and clinically feasible to conduct further clinical studies.
We use our artificial intelligence driven technology to discover, prioritize and market validate indications. We identify and filter identified and filtered across multiple dimensions, thousands of candidate indications by leveraging the Onto-Hypersearch capabilities of the Ontosight platform. We evaluate evaluated the importance of each indication, predict predicted the likelihood of success and market validate validated the best candidates based on competitive analysis.

To discover and prioritize indication, our approach consists of the following steps:
Pathway Correlation and Simulations: To identify pathways based on literature mining and publicly available databases.

We also leverage our research graph including PPI, co-expression studies to identify the 1st tier and 2nd tier pathways: Indication identification, Literature and evidence-based Analysis, Assessment of Biological mode, Estimate of trial complexity & feasibility.