Incidence of Drowsiness / Somnolence was modeled as a function of C max (IR and MR data from 3 studies at 6 doses) Historically many companies have approached pharmaceutical development decisions with considerable ambiguity as to how available options impact the net patient benefit. In particular, since many drug features “trade off” (e.g., more efficacy is typically accompanied by increased problems with safety or tolerability), it is difficult for company decision-makers to know the optimal combination of “knob settings” among their formulation options. Another challenge is the increasing need for drug companies to revisit existing drugs for continued profitability. Discovery of new drugs has diminished in recent years. Thus companies are looking to reformulations as a means of finding new ways of differentiating their products and offering patients benefit. Meanwhile, the use of analytical techniques such as Modeling and Simulation (M&S) and Decision Analysis (DA) has increased. These methods are enhancing pharmaceutical companies’ ability to make clear formulation decisions before making huge investments. This presentation will provide an example of linking M&S and DA to a formulation decision. The analysis involved a PK/PD M&S exercise that, when combined with a Clinical Utility Index (CUI, a multi-attribute evaluation), gave the client an ability to see if altering the formulation of a drug could improve the net patient benefit and thus further inform the investment decision. The analysis was to inform a vital decision and consequently involved stakeholders from many functions and levels The client’s project team: ●Clinical Development (VP) ●Project Management ●New Product [Commercial] Development ●Biostatistics and DMPK (periodically) This was a strategic, portfolio-level effort to decide whether the program could be sufficiently worthwhile to warrant further investment. Senior management participation and buy-in were key to the success of the project. Dopahexidine is a mature drug with substantial historical data and significant tolerability issues Dopahexidine is indicated for a chronic neuro-muscular disorder. Efficacy is closely related to drug levels (concentration in plasma). ●Measured according to a standard efficacy scale (SES) Incidence of adverse events is also related to drug level. Dopahexidine is eliminated quickly. ●To achieve acceptable duration of effect, relatively large doses must be given multiple times daily ●This implies brief exposure to high plasma levels and increased incidence of concentration-related adverse events Published data did not necessarily include ideal PK/PD information. ●A number of small studies over several years ●Studied either PK or efficacy, but not necessarily at the same time ●Literature was not particularly systematic Dopahexidine is an anonymized mature drug indicated for a chronic neuromuscular condition Critical Business Decision Is it worth trying to find a new formulation of this drug? Transitioning to Tactical Questions 1. Can the main efficacy and tolerability characteristics be predicted from publicly available summary data? 2. Can sufficient additional patient benefit be provided by an alternative formulation to warrant further development? Several attributes of the drug were relevant to development decisions. A Clinical Utility Index (CUI) provided a single metric for decision-making. Every drug has benefits and risks. As a result, tradeoffs have to be made among the drug attributes in the product profile. The relative importance of these characteristics depends on the indication, patient population, and decision at hand. The Clinical Utility Index (CUI) quantifies these tradeoffs by providing a single metric for the multiple dimensions of benefit and risk. ●It is…a systematic approach to understanding subjective preferences a transparent way of weighing tradeoffs knowledge-driven; available data are used; if not, we rely on expert opinion closely related to the Target Product Profile a Multi-Attribute Utility Function ●It is not…an “objective” measure in the sense of a physiological measurement, such as blood pressure. CUI Elicitation: The Clinical Utility Index assessment process has several distinct steps 1.Determine the critical attributes affecting the utility of the treatment: the product profile Examples: Major efficacy endpoint, adverse effects, compliance-related issues, including those affecting key competitors 2.Define response levels for each attribute ●Example: A particular marker could show an effect Worse, Equivalent, or Superior relative to the standard of care or a key competitor ●Responses to continuous variables are discretized ●Categorical variables fit naturally in this framework 3.Quantify relative preference for the levels of responses within each attribute 4.Assign weights to the importance of the attributes relative to each other BACKGROUNDTHE PK/PD MODELSTHE CLINICAL UTILITY MODEL Meaningful response ranges and their preference values were assessed The clinical team identified and ranked the most important attributes of the drug Weights here are the inter-attribute relative weights. Note that U (utility) is either expressed on a {0,1} or {0,100} scale. In this example, the NCE has a somewhat higher utility for Effect 1 efficacy but a substantially lower utility for AE 1 … thus its CUI is lower than the comparator. With this complete, we were in a position to examine the dose-response for Clinical Utility and look at sensitivity to particular attributes. Example calculation for a simple CUI: COMMUNICATING MODEL FINDINGS CONCLUSIONS The PK Model was based on a pooled analysis of 6 studies A single compartment model was adequate SES –Two studies at three doses with data were suitable for modeling For systolic blood pressure, two trials at three doses were available Model of dyskinesia was problematic due to lack of consistent data We used DMX to explore and communicate models findings for the major endpoints We explored the effects of altering PK characteristics We also examined the effects of alternative formulations on overall patient benefit, as quantified by the CUI Sensitivity analysis for CUI – duration of efficacy drives CUI more than does drowsiness / somnolence Graphs show simulated time course for Cp and SES for an 8 mg dose. Tables give quantitative predictions for arbitrarily selected times. Graphs show an increased duration of effect at a higher dose, with absorption rate decreased by 75%. Time of onset (not in CUI) and maximal change in SES are similar. Duration of efficacy is substantially increased. Graph and table compare various doses of a new formulation (with absorption 20% as fast as current) versus the best dose of the current formulation The plot shows how much better 24mg with slow absorption (Ka factor of 0.2) is versus the best dose with “current” absorption, 8 mg. Note that when we remove drowsiness / somnolence as a differentiating attribute, there is little effect on the plot (green and brown lines are almost on top of each other). Meanwhile, when we remove duration as a differentiating factor, there is a bigger change (green versus purple). Thus the major driver making 24mg with Ka factor of 0.2 better than 8mg at “current” Ka is duration. “Can the main efficacy and tolerability characteristics be predicted from publicly available summary data?” –Information was gathered information from a variety of public sources. –PK/PD models were constructed describing the main efficacy and tolerability endpoints. –Input was gathered from a variety of internal stakeholders and integrated into a single metric of clinical benefit. “Can sufficient additional patient benefit be provided by an alternative formulation to warrant further development?” –Outcomes were simulated for a variety of drug absorption scenarios. –Results of the simulations were explored with the project team, leading to…  Specific, actionable recommendations for a reformulated drug that is likely to have superior benefit to the current formulation. Supported by these insights, this project continued development Did we address the tactical questions (and thus the strategic decision)? Key learnings from this example of integrating modeling with drug design to inform decision making Enabling software that lets non-modelers "play" and test their intuition and assumptions can be a powerful way to unite models with $$ impact. Techniques like CUI put unwieldy dimensions into a common currency. To make M&S more regularized and accessible, we must continue to focus on communication. Development of staff who can “speak the language” of both modeling and business decisions. We were now in a position to explore the implications of our models together with the team… DMX ® is a software visualization and communication tool to explore M&S results Used by modeling experts to make M&S results available to teams and decision-makers Used by the project team to compare performance vs. competing treatments, evaluate product profiles, and understand trade-offs Used by teams to capture knowledge and update as new information becomes available THE INTEGRATED MODELCASE STATEMENT Linking Modeling & Simulation, Decision Analysis, and the Technology of Drug Formation Kevin Dykstra, Lee Hodge, Bob Korsan, T.J. Carrothers. Pharsight Corporation, Mountain View, CA Dose of new “Ka x 0.2” formulation (mg) Data (IR and MR) from two studies at five doses. Unless the uncertainty could be better resolved, this attribute would not influence the decision. Dose of new “Ka x 0.2” formulation (mg)