IPQ enables the representation of “what we think we know” but focuses on enabling exploration and reasoning of the real-world complexities that go beyond “what we need to know”. IPQ’s solutions extend from several basic tenets:
INDUSTRY (Healthcare & Life Sciences)
Healthcare, Life Sciences, Emerging Biopharma, Diagnostics, Biotech - Globally
- Discovery: using novel stratification, i.e. next generation phenotyping, to more specifically identify targets, enable earlier business decisions about patient population size, enable more targeted and smaller clinical trials to be developed
- Clinical Development: enhance development of inclusion/exclusion criteria,identify and close the gap between RCT patients and real-world patients, support recovery of failed clinical trials, using stratification of RWD patients, to replace subgroup analysis and/or identify out-licensing opportunities.
- Medical Affairs: support translation of approved drugs into clinical practice, i.e. comparative effectiveness is more than “comparative efficacy”, it needs to consider if the physician will prescribe the drug and whether the patient will take the drug…otherwise a regulatory success can be a commercial failure
- Portfolio Prioritization: IPQ’s model that addresses the complexity of the patient, of the disease and of clinical practice contribute significantly to the decision-making process for portfolio optimization, i.e. IPQ’s approach to comparative effectiveness noted in Medical Affairs, above
Completed Project Examples:
- Failed clinical trial recovery and drug repositioning
- Model for patient/physician acceptance of new drug product
- Systems analysis of COPD and tobacco use
EXAMPLE: Clinical Trial Recovery
The Problem: A large clinical trial failed to show effectiveness for a drug in treating HFpEF; conventional subgroup analysis also failed.
IPQ’s Applied Solution: Evaluated the basic diagnostic criteria, recognizing disease is a process, applied “next generation phenotyping”(NGP) to stratify the disease based on disease progression using real world patient data, not clinical trial data
Benefits: IPQ’s analysis of real-world patient data using NGP, enabled re-diagnosis of trial patients. Additionally, We identified five unique patient subgroups based on clinical presentation, independent of trial performance...
RESEARCH (Non-Commercial)
Institutions, Agencies
- Population-based studies: evaluation of national patient databases to evaluate patients of appropriate/inappropriate prescribing patterns, impact of government policies on substance use, early detection of critical health conditions
- Disease Modeling: disease risk modeling for prevention and early intervention, development of novel biomarkers
- Data Models and Infrastructure: design and development of regional level tissue and data repository, design of major NIH-funded program multi-center infrastructure
- Advanced Analytics: development of clinical research/clinical decision support system
Completed Project Examples:
- Evaluation of polypharmacy in national health records
- Early detection of frailty risk in clinical practice for the elderly
- Impact of government policies on tobacco use
- Public assessment of Covid awareness: Involving the Patient
- Systems analysis of COPD and tobacco use
- Design and Implementation of Lifelines data model and system
- Design and implementation of Immune Tolerance Network data model and system
- Pediatric rare disease foundation clinical decision support and clinical research platform
EXAMPLE: LEARNING-CVD; Dynamic LEarning Approach to Risk stratificatioN to Improve predictionN, prevention, diagnosis and monitorinG of CardioVascular Disease
The Problem: Cardiovascular diseases (CVDs), including atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) are the leading cause of death globally, accounting for 18 million deaths and affecting 523 million individuals. HF and ASCVD affect a significant portion of these individuals and numbers are growing.
- Mortality rates are high, and morbidity of CVD patients is becoming a significant problem.
- In Europe, CVD is the most common cause of death, with nearly 4 million people dying from CVD per year, 22% of those dying prematurely (aged <70 years).
- CVDs can be prevented or managed with appropriate interventions.
- Approximately 70% of the burden associated with CVD is attributed to risk factors which can be controlled or modified.
With the high impact of CVD, there is a need to move beyond the current approaches with respect to risk assessment. However, there is no empirical data to guide intervals for risk assessment or the trajectories of the accumulated risk over the years.
IPQ's Applied Solution: Shift the paradigm to recognize disease as a process. LEARNING-CVD address this by going beyond the state of the art to develop a model that represents the journey of a patient that starts pre-disease, as currently defined, includes more accurate risk analyses and enhances
the opportunity to empower patients and physicians to prevent progression. LEARNING-CVD innovatively addresses existing challenges by adopting, adapting and developing methodologies that describe the whole patient journey, aggregating (or generating) critical data while recognizing multiple levels of regulatory control as well as inherent biases, and modifying and evolving
analytic methods to move beyond correlation and approach causality.
Benefits: LEARNING-CVD’s reducing the impact of CVDs, with an emphasis on HF and ASCVD, through the development of a dynamic learning model...
PAYERS
Domestic and Global Entities
- Disease and patient stratification: to improve reimbursement for services, e.g. diagnostics and treatment/drugs
- Extension of Social Determinants of Health to Cultural Determinants: to optimize delivery of long-term social services to elderly immigrant populations
- Dynamic systems modeling: design of COVID vaccine distribution model to support continuous monitoring and needs evaluation
Completed Project Examples:
- Long term social services for elderly immigrant populations
- Redesign of regional healthcare clinical/research data environment
- Development of dynamic Covid vaccine distribution model
EXAMPLE: Continuous Digital Monitoring,
Pre-Clampsia in Pregnancy (Maternal Healthcare)
The Problem: Pre-eclampsia is persistent high blood pressure that develops during pregnancy (5-8%) and is a leading cause of illness and death globally for mothers and infants.
- Hypertensive disorders of pregnancy (HDP) complicate roughly 10% of pregnancies worldwide and approximately 10–15% of maternal mortalitycan be attributed to pre-eclampsia or eclampsia
- Pre-eclampsia can result in premature births, resulting (80%) in BPD which affects early development and later lung and neuro disorders in children
- The pre-eclampsia rate is 60 percent higher in black women than in white women and black women are more likely to develop severe pre- eclampsia.
Multiple methods (~20) for measurement exist and do not provide identical measurements. Although specific guidelines for “how to measure blood pressure” exist, clinicians do not follow them for blood pressure follows a diurnal pattern that is not typically incorporated into the evaluation for Hypertension.
IPQ’s Applied Solution: Established collaborations with companies involved in continuous blood pressure monitoring and programs with extensive data on diurnal blood pressure measurements. We analyze both diurnal and long-term variations based on high resolution features that can be related to underlying biological/physiological processes for disease risk evaluation, potential prevention, treatment response.
Benefits: Produce a significant enhancement in patient care, risk assessment, cost-containment and patient management by increasing understanding of causal relationships particularly in the case of infant/maternal morbidity and mortality and in underserved populations...
INVESTMENT BANKS
INVESTMENT BANKS
- Due diligence: quantitative evaluation of targets for investment or acquisition
- Portfolio prioritization: support of portfolio company prioritization of assets/opportunities
- Comparative effectiveness modeling: extending current “comparative effectiveness” modeling beyond comparative efficacy to evaluate physician and patient response for full value assessment
Completed Project Examples:
- Due diligence: Molecular modeling companies
- Investment analysis for upcoming product PDUFA evaluation
- Failed clinical trial recovery and repositioning (portfolio enhancement)
EXAMPLE: CONFIDENTIAL
Transforming Precision Medicine
into accurate Medicine