Welcome to Module 4, the Foundation of Modelled Informed Drug Development, MIDD, and Modelled Interconnectedness. This presentation will provide you the foundation knowledge of MIDD to other pieces of training in the series of Modelled Interconnectedness. The MIDD Modelled Interconnectedness series are developed by Adekimi Taylor, Rajash Krishna, and Amy Chung, who are Senior Directors at Cetara.
and in collaboration with Critical Path Institute. In the next few slides, we will talk about the foundational principles underlying Model-Informed Drug Development or MIDD. There are many drivers of uncertainty within drug development.
These include confidence in targets, confidence in the drug in question, confidence in the chosen endpoints, and confidence in regulatory decisions. So MIDD can be viewed as a rational approach to effectively and efficiently accelerate drug development. And as you can see in this inset, these are the five different objectives that you see. And that can range from various components of these drivers including pathway, target, drug, risk benefit, and payer perspectives. And within each pathway, there are tools and techniques that can help answer the question whether this is the right pathway to pursue, right target to consider, right molecule to develop, are we picking the right dose, and do we have enrolled the right patients.
When looking at this in an interconnected manner, they convey a story that underlines the drug product development ecosystem. In this slide, we reflect on how model-informed drug development can help with key development decisions. As you look towards the continuum of discovery, preclinical, early clinical, late clinical, and commercial, which is the horizontal arrow that you see on the top of the slide. There are very specific questions that one needs to take into account within each phase of development. For example, within discovery, the key questions we need to answer is, do we know the target?
Do we know the metabolism transport of the drug in question? Can you design the molecule outside of the patent landscape that can differentiate you from a competitor, for example? What is an optimum synthetic pathway? What's the risk of off-target effects?
So these are some of the questions within discovery phase. As you move towards preclinical, questions are more tailored towards, do we have the right information to consider before going to early clinical? For example, what compound to pick?
What should be the first in human dose? Are there likely to be drug interactions? What's the risk of cardiac safety?
And do we have a biomarker that can best define the exposure response strategy? With an early clinical, the questions are separate and perhaps interconnected with both preclinical as well as late clinical. This includes, do we have the right trial design to show proof of concept? Are we testing the best dose?
What is the risk-benefit profile? And are there subpopulations of interest? What is the efficacy and safety of combination therapy that could also be pursued with an early clinical phase? And this is also a time when you have a preliminary idea of how good of a product you have.
Relative to either a standard of care or a competitor product. And finally, a very critical question is, have we made the right investment decision? Because once you move into late clinical phase and you start work within that phase, the risk of making a wrong decision could be a financial challenge. Some of the questions in late clinical, for example, include.
Should we continue with this development program? What is the therapeutic window for this drug? How can we optimize the study design? What is the optimum inclusion-exclusion criteria which could inform labeling? How do we price a drug?
Do we have the right market access plan? What is the strategy for pediatric development? As you get into commercial space, these questions have more proximal benefit for approval, but also launch. And these include benefit-risk assessment, real-world evidence. about return of investment, payer access perspective, what additional indications to pursue, should we consider lifecycle management, and so on and so forth.
Again, within these questions, you already can see varying degrees of interconnectedness of these models. To allow MIDD to influence various decisions during the different phases of R&D. It is essential to implement a MIDD strategic plan.
The development of the MIDD strategic plan is supported by the five different MIDD tools. The first step in the plan development is to identify the key R&D questions where MIDD will provide impact. There are seven different themes of questions that can be asked concerning the compound, mechanism, and disease or indication levels.
To expand, the three key levels are disease level, questions and activities that can be answered in advance of the development of any particular compound, compound level, questions and activities that focus on the data associated with a compound of interest, additional mechanism level considerations, compound level, or disease level questions and activities where there is a focus on the mechanism of action and the knowledge gained from other compounds with a similar mechanism of action. For the seven themes they are the medical need or commercial viability, R&D question related to the understanding of the medical need and the areas of potential differentiation from the standard of care, for a particular disease or indication. This can inform the likelihood of a particular compound achieving the important aspect of a product profile at each stage of development. Efficacy.
R&D questions related to the characterisation of the dose exposure response relationship for important efficacy outcomes. Safety or tolerability. R&D questions related to the characterisation of the dose-exposure-response relationship for importance, safety or tolerability outcomes. Pharmacokinetics.
R&D questions related to the characterisation and extrapolation of the pharmacokinetic properties of a drug across species and patient populations. The general expected impact of a progressive disease state. Intrinsic, for example, age, organ impairment, or extrinsic factors, for example, co-administrated drugs, and the influence of formulation or administration method on drug exposure. Benefit or risk are the questions related to the definition and quantification of the relative trade-offs between important efficacy and safety outcomes to determine optimal dose regimens.
that are sufficiently effective and safe. Clinical viability, R&D questions related to the assessment of potential development programs for a particular indication, considering options concerning populations, subpopulations, inclusion or exclusion criteria. Study design, R&D questions related to the optimal design of the subsequent studies, balancing the cost, and the time of the current study versus anticipated future risk given the predicted confidence in achieving the required product profile. Once the key questions are identified, the appropriate modelling approach or combined approaches, which is our model interconnectedness, will be selected along with the assumption settings and evaluation strategy. And finally, the impact level assessment will allow us to evaluate how the MIDD impacted the decision-making from both industry and regulatory perspectives.
The impacts were partitioned into three categories, descriptive, justified, which means decision informative, and evidence substitution. Different key questions can be asked at different stages of drug discovery and development process, from target selection and validation, non-clinical phase, clinical phase to life cycle management. If we choose one of the themes, as efficacy. You can see different questions can be asked for disease level, compound level, and additional mechanism level.
Different types of quantitative approaches can be applied to answer them, which allow placing the puzzle together. For example, at the disease level, the question would be what are the dose-response relationship for the compound used to treat the disease. A comparator database can be built from the literature for known compounds or placebo used in the disease or indication of interest to support explorations and model-based meta-analysis and BMA. Previously, we mentioned assumption setting and validation strategy is part of the fundamental key step for MIDD, especially when the data and information are sparse.
There are five types of important assumptions, namely pharmacological assumptions, physiological assumptions, disease assumptions, data assumptions, and mathematical and statistical assumptions. The key is the assumption will need to be clearly identified and documented prior to any MIDD processes. as the assumption will be tested and evaluated if testable, which will impact the next modelling steps and decision. The assumption is also very important for all types of modelling.
For example, in empirical modelling, as the model is quite simple, many assumptions need to be made, for example the number of compartments based on the profile. For mechanistic model, as the model is quite complex, Assumptions need to make, for example, prior knowledge of certain biological pathway processes, rates from the literature. For extrapolation approach, the assumption must be both presented in the plan and also in the report of how the assumptions can be validated and tested.
Sensitivity analysis is also a very important way to validate assumptions. Again, assumptions should be clearly stated and documented at the submission stage or when interacting with internal stakeholders, external investigators or with regulators, patient groups, because this will allow more transparencies to the uncertainty and certainty and allow better discussions and communication. The MIDD impact level allows evaluation of how the quantitative evaluation approaches impact decisions, which varies from the audience and the approaches used.
The classification range from low, medium to high. It is a system to rank how impactful a piece of quantitative work is to internal decision making. Low impact is when the work is used to describe the data. So moderate in terms of decision fitting, data to a PK model at the end of the study analysis. Medium is when the work can influence a decision, for example population selection, study design, etc. using simulation or clinical trial simulation.
High is based on when not additional data or experiment is required to impact on decision, for example using extrapolation to waive an unnecessary clinical trial. This system also ensures we can classify the type of work and the associated power of MIDD. With the model interconnectedness, it allows knowledge to keep building up and knowledge keep propagating and this will increase the probability of high impact work to decision.
Next, we will discuss the types of models that can be considered during drug development. There are different ways to classify models. The classification system I will use is based on the white paper on good practices in modeling.
by the European Federation of Pharmaceutical Industries and Associations Model-Informed Drug Discovery and Development Workgroup. The first type of model we'll discuss is the empirical dose exposure time model. This type of model relates doses or exposures such as the area under the concentration time curve or other summary measure of drug exposure to the pharmacodynamic response. and there could also be a component of time. This type of model uses simple empirical functions to describe the drug effect.
For example, it could be a linear drug effect. It could also be a hyperbolic drug effect model, more commonly known as an Emax model. And in this type of model, there is a limit beyond which the drug effect can no longer increase, even if drug concentrations increase. Another model is the exponential type of model.
This is not an exhaustive list but just an example of the types of simple empirical functions that are used which are not mechanism based. This type of model uses limited assumptions and as a result it has a limited extrapolation potential. The example on the left is a model for Alzheimer's disease that describes the change over time in the ADAS cognitive score, which is an efficacy endpoint for Alzheimer's disease, at different doses of the investigational drug.
And so it looks as the change over time. So this would be a dose-time model. In the second example, the drug response is related to the drug exposure, in this case the area under the curve, using a saturable or Emax model. and this can be used to predict the response rate range for different dose levels as you can see is being done on the curve and so based on this type of model you can read of the range of exposures at the different dose levels which are represented by the horizontal bars near the bottom of the graph and then you can read off the response rate for each of those dose or exposure ranges.
The next type of model is the semi-mechanistic pharmacokinetic pharmacodynamic or PKPD model. This type of model is based on what we know about the biological and pharmacological mechanisms of the drug and can use data from different sources. It can use human data that is clinical data or preclinical data which would include animal data as well as in vitro experiments and these can be used to add mechanistic interpretations to the model parameters. We can think of the types of parameters of the models as consisting of the drug parameters and the system parameters.
The drug parameters would be those parameters that are specific to the compound for example the pharmacokinetic parameters as well as the drug effect parameters such as the drug potency. The system parameters would describe the system that is being modeled, for example the synthesis rate of a biomarker being modeled. These models are more complex than your empirical PKPD models and also have somewhat of a mechanistic basis and as such they can be used to extrapolate beyond the data that were used to model the to create the model the example i am showing here is of a population pkpd model for serum intact parathyroid hormone concentrations or ipt which would be our biomarker endpoint this model shows one of the simplest types of semi-mechanistic pkpd models which is called the turnover model.
It shows how the plasma concentrations of the drug impact the synthesis rate of IPTH. And so here you can see that there's a synthesis rate of IPTH and also an elimination rate of IPTH, which under circumstances where there's no drug intervention, balance out to maintain homeostasis. But when you add the drug concentrations, the drug then reduces the synthesis rate of IPTH. Here we get a glimpse into the interconnectedness of models as we see how the pharmacokinetic model is used to predict the concentrations that will drive the pharmacodynamic effect.
In the graphs below we see the mean model predictions shown as lines overlaid on the mean observations which are shown as the open circles. The left graph shows the drug concentration profiles at various doses. which are generated by the PK model. And on the right, we see the model predictions of the hormone concentrations over time at the different doses.
And as you can see here, this model does a good job of predicting the concentration time profile profiles of IPTH. Next, we come to model-based meta-analysis. This type of modeling approach uses summary statistics such as mean, median, standard deviation of trial data to assess the comparative risk-benefit ratios of different compounds of interest.
Previously, we have seen the other models looking at individual-level data. These models look at trial-level data, and they typically use data found in the literature or in public databases. These models will account for the impact of the treatment, but not only that, they will also account for differences in the patient populations across the different trials, as well as trial characteristics.
They can use empirical models, or they can use models that account for the clinical pharmacology of the drug. And they can also be used for extrapolation if they are part of a disease progression model. I know that...
application is being used in estimating scaling factors. This example shows how a model-based meta-analysis was used to describe and predict the response over time to various osteoporosis drugs, which are shown in the legend on the right, and their effect on increasing bone mineral density. On the left, you see the effect of the various drugs in increasing lumbar spine bone mineral density and on the right, total hip bone mineral density. And these drugs, these models can be used to see how to position the drugs under development and to compare them to drugs that are already on the market. Now we'll talk about quantitative systems pharmacology or QSP models and physiologically based.
PK or PBPK models. These models are being grouped together because they are similar in that they are both bottom-up models. What this means is that these drugs use an understanding of biology, physiology, pharmacokinetics, pharmacological processes, and the pathology of the drug to predict drug exposure as well as the drug effects which could be either therapeutic or toxic effects.
So these drugs are based on what we know about the systems under consideration. Unlike the other models which we have looked at, which are typically looking at using the data to develop the model structure. And so in that sense, they are considered to be top down models. PBPK models and QSP models need data from a variety of sources.
For example. They will have data on blood flow rates, which is based on the biology or physiology of the system, and they could also include data about the drug binding characteristics and also the drug physicochemical properties such as permeability or solubility. These are multi-scale models, so they encompass the target level up to the cellular level. and ultimately up to the whole body level.
And as a result of their complexity, they consist of systems of many differential equations. The example I'm presenting here shows a PPPK model for monoclonal antibodies. As you can see, the model describes the monoclonal antibody transport and partitioning. into the different organs and plasma.
which are represented as compartments. The model relies on blood flow rate and blood flow rates in the various organs as well as reaction and binding at the tissue level as you can see here. Because there are many compartments this model will consist of many more ordinary differential equations than your typical empirical PKPD. model or semi-mechanistic model. Due to their bottom-up nature, these models are more often used for predictions rather than to fit data, although predictions are typically compared to the observations in order to validate the model.
As we can see from the plots on the right, the PPPK model was able to provide good predictions of concentrations in the plasma and in different organs of knockout and wild-type mice. Epidemiology models describe the spread of disease and the duration of epidemics, and they show how individuals in a population move from one status to another. For example, they could move from a status where they are susceptible to an infection, to infected, and to a recovered status.
They can use either ordinary differential equations or be based on a stochastic framework. And they also are not limited to experimental studies but they can also use observational studies and these models can also be used for extrapolation and they often are. The model in this example describes how individuals in the population move through various states from susceptible to influenza infection to exposed, infected and eventually recovered under scenarios of treatment with oseltamivir.
On the right, we see a model fit of the fraction infected over time, represented by the dashed line, and the observed fraction of population infected, which is shown with the solid line, and we can see that the model predictions correspond well with the data. Health economics and outcome research, or HEOR models, are models that provide economic analyses of healthcare interventions. These types of models establish the efficacy of an intervention and compare it to the effectiveness of other interventions, especially existing interventions for the indication in question.
These models do not only consider efficacy but also consider the incremental cost efficiency and this is used to determine the optimal clinical application for the intervention, and also its overall economic value. Decision makers use these types of models to help to select the best intervention for a given patient population, and they are also used by regulatory agencies and governments to determine reimbursement structures for different drugs. Our example shows Health Economics and Outcomes Research Modeling to compare the cost-effectiveness of pembrolizumab to the standard of care in treating patients with non-small cell lung cancer. A partitioned survival model was developed and this model had three mutually exclusive health states as shown in the top left figure. The patient starts in a progression-free state then experiences progressive disease and eventually death.
Alternatively, death can also be due to other causes. Outcomes and costs of adverse events were also incorporated into the model, and for patients receiving each intervention, the cumulative total costs and health outcomes over the time horizon were estimated using the time in each of the modeled health states. Shown at the bottom left are the clinical parameters of the model, as well as a list of resource utilization. and cost inputs.
In the next part, we'll go into more details about the interconnectedness of models. In the field of engineering, biology and medicine, we can consider all the processes we want to investigate who has an input, a main system and an output connected. This is represented by the yellow, pink and green bubbles in here. As of all the processes, There will also be a flowed and propagation of information and feedback learning through multiple iterations.
It enabled us to establish a learning and confirming cycle. If then we consider from a MIDD point of view, the input includes the key questions, data availability, assumption of data and model, disease or compound based on prior knowledge. This will then fit into the types of models to be chosen and how the different modelling approaches should be connected to develop a framework to answer the question which resulted in our interconnectedness. The output can result from each step of or each type of modelling work as a result of the model interconnectedness to produce an overall impact on the decision making.
Here I want to emphasise that the impact can happen in various stages of drug discovery and development process, ranging from the design of a clinical study, assessment of a compound potential, go no-go decision and regulatory submission. Again, the interconnectedness allowing us to learn as we go along, which is like putting puzzle pieces together. MIDD can apply to all stages of drug discovery and development. It increases our knowledge and propagates the information to allow us to have the full package and confidence of drug and disease. The key questions and data steer the choice of modelling approach.
and how interconnectedness can establish. Through the learning and confirming cycle, assumptions will get evaluate, confirm and update as the process continue, which decrease our uncertainty and increases the confidence of the drug and disease. A very common type of model interconnectedness is the use of population picking models to derive concentrations or exposures. that are used as inputs to other models.
Another type of model interconnectedness is multi-scale models that can include different types of models. In this example, we see how several different types of models were used to describe the local delivery of the chemotherapeutic drug doxorubicin to tumors. The top set of figures represents the physiologically based drug delivery model consisting of compartmental PK models to describe the kinetics of doxorubicin release from the liposomes.
The PK model is connected to the tumor compartment, shown in the bottom left figure in blue. Figure B on the bottom right shows the detail of the tumor model on a cellular level, which can be considered a physiologically based QSP model that describes the transport of doxorubicin in and out of the tumor cells and includes transport through the cell membrane and the cytosol, as well as reversible binding to DNA in the nucleus. Different types of models can be leveraged during different stages of development as shown here in this example, which will be elaborated on in one of the case studies. In summary, the development of pembrolizumab used different types of models as shown here.
during different stages of drug development for internal decision making such as study designs and also to support registration and labeling. In addition to model interconnectedness within a compound, the interconnectedness can also apply to understand any disease. Here illustrate how using various modeling approaches from disease progression model PKPD to QSP to learn about the disease.
the mode of action of the compound, how compounds of different classes are different when targeting Alzheimer's disease, the safety of a compound in terms of QT prolongation, and finally how using QSP model can provide scheduling decision during COVID-19 interruptions. Often the output from one type of model will become the input for another model types. For example, with disease progression models from an individual level's data, it can be combined and analyzed data from the literature using aggregated levels of data to form a model-based meta-analysis.
Perius publications, you can see from the diagram at the bottom left corner, defines parallel drug disease modeling at different stage of R&D. as you can see in the x-axis and answering different key questions at different levels which is at the y-axis such as right target, right tissues, right safety, right patients and right commercial potential. However with interconnectedness here we propose that we can combine this to diagram and the different approaches can answer all the questions at different stages on R&D. An important note is that the application of PPPK and MBMA don't restrain to answer certain questions at a specific stage of development, for example either as early stage or late stage of development. Instead, both approaches can be used for translation and analyzing clinical data.
For example, the usage of MBMA can utilize non-clinical and clinical literature data to answer questions on tissue, safety, patient population, and right commercial, for example by having an early head-to-head comparison. This slide reflects on the general theme of model interconnectedness and And as we can see here, there are multiple aspects to this model interconnectedness, which can range from as simple as PK or PK-PD or PB-PK models, but it can also be more mechanistically complex, and that could have an effect based on the disease focus, wherein there are multiple disease-specific platforms. and quantitative systems pharmacology models that come into play.
As you go more into the market-facing studies, more questions emerge about model-based meta-analysis, clinical utility index, as well as pharmacology-to-payer models, where you have stakeholder assessment, including the payer, sponsor, regulator, and more importantly, the patient. So this schema reflects the modeling ecosystem, which comprises of intricate models interconnected by various opportunities as the molecule transcends through various discovery and development interfaces into the marketplace. Next, we'll discuss the different considerations when selecting a modeling approach. The level of complexity and interconnectedness will be depending on the purpose and the key questions of the analysis and the availability of data and prior knowledge.
For example, in a clinic where we have a combination of rich PK sampling in phase 1 and sparse sampling in phase 2, the population PK modelling approach using an empirical compartmental modelling approach would be ideal. However, in a situation where physiological information is relevant in the modeling, for example including the organ size differences and maturations in pediatric population, we would consider the application of physiological-based PPPK modeling. It is important to note that the top-down and bottom-up approaches can be applied together instead of carrying out independently.
had therefore knowledge exchange and pass through the development stage of the different models. An example will be using non-clinical translational PPPK modeling for first time in manned projections while then using the PPPK model to simulate the populations in phase one. After that using this simulated phase one data to develop an initial POPPK model for optimal design for design of the PK sampling for the new trial.
Before we embark on selecting the type of model you want to use, we need to understand the goal of the modeling exercise. And based on that goal, we can then select a fit for purpose model that can accomplish that goal. And that fit for purpose model could be something as simple as a back of the envelope calculation that calculates the dose needed to achieve a certain exposure based on non-compartmental analysis data or it could be something as complex as a QSP or a PBPK models.
So some of the things we need to consider are what questions do you need to answer and how will you apply the model? What data do you have and what do you know? The data you have and what you know about the system or the drug are going to restrict the amount the types of models that you may be able to use at the moment Another important thing is how long do you have to develop and apply the models?
If you don't have a very long time, you may have to settle for a more simplified model. And importantly, what resources do you have? For example, what kind of computational resources?
Do you have enough computational power to develop a very complicated model? Also, human resources. What kind of skills do the people who are going to be developing the model, what kind of skills do they have?
Let's talk some more about available time and resources. So if you need answers quickly, some of the models that you could use would be the dose exposure time models, empirical PK and PK PD models, model based meta analysis, assuming you already have a database available for you to use as data for modeling, creating your models. And interestingly, PBPK models.
The reason I put PBPK models on this list, even though they are more complex, is that. They are typically relatively fast with dedicated software and there's commercial software available specifically for PPPK modeling. However, we have to consider that PPPK models require a lot of data from different sources. So if additional experiments are needed to provide input into the PPPK model, the time that that's going to take needs to be taken into consideration.
If you have more time available and it's appropriate, you will want to look at more semi-mechanistic PKPD models or PBPK, which appears again, and QSP modeling. A note is that semi-mechanistic PKPD and QSP models can be very time and computationally intensive. They're also more complex and more difficult to implement, so you need to keep that in mind. Next, we'll discuss the different types of data used in modeling. A lot of the models we've discussed use individual level data or subject level data where we're taking data from particular individual subjects or animals for example if it's a preclinical data, but we can also use aggregate level data in certain types of models.
Aggregate level data are data that are summarized across individuals. For example, this would be data summarized across individuals in a treatment arm. For example, for a diabetes study, it could be data showing the mean change in HbA1c levels over time in different treatment arms. So you have the mean, median, standard deviation, etc. for each treatment arm. These are often derived from published data, but of course the sponsor can aggregate the data from their own internal studies.
Aggregate level data are most commonly associated with model-based meta-analysis. However, they are also used in health economics research and epidemiology models. Most of the data that we've been discussing so far are data collected from clinical trials or from planned experimental studies in animals or in vitro studies. However, we also have real-world data, which are data collected outside of clinical trials, and they can come from...
various sources. Some of them are electronic health records, they could be health surveys, they could even be health-related apps on your mobile device or social media. These types of models are used sometimes in epidemiology and health economics and outcomes research models.
And here I'm presenting a paper by Jeffrey Barrett and Penny Heaton, in case you're more interested in seeing the potential of using real-world data for global health. Here I'm going to categorize data as system property data and drug property data. The system property data are the data relating to the system that's under consideration which depends on the disease of course and so those will be data that we get from our knowledge of physiology and also the pathology of the drug and these would be things like organ size, the blood flow rates to the different organs, hematocrit, biomarker levels, and also the pathway that the drug is acting on, as well as the drug target. Drug properties are properties that are intrinsic to the drug itself and are independent of the system and they determine how the drug interacts with the system.
And these are typically gotten from in vitro experiments. There are things like molecular weight, lipophilicity, permeability, binding kinetics, and even the enzymes that metabolize the drug as well as the fraction metabolized and the fraction eliminated by other means such as renal. These are the inputs that usually go into PBPK and QSP models.
However, some of these inputs and some of these data system and drug property data can go into some more complex semi-mechanistic models. Extrapolation is a powerful application of MIDD, and it is important to have a thorough strategy to minimize the potential unnecessary clinical studies. in certain pediatric groups with existing data.
Basic MIDD framework of key questions, assumption, modeling approaches and documentation not only allow to support extrapolation strategy, but with a successful application, it will support the development of regulatory guidance. For example, extrapolation using population PK modeling approach in partial onset seizures. However, in the future we should increase to consider the use of various modeling approaches or interconnected approaches to support the assessment of similarity of disease and also extrapolation strategy and validation of the extrapolation strategy.
In this slide we bring in the various components of what we know about models. as it relates to translating a clinical trial patient to a real-world patient. We facilitate that understanding using models.
One of the key components of the modeling process is translational aspects of that modeling process. Now, within the discipline of translational sciences, you can relate an observation from a set of in vitro assays progressing into a preclinical species, further progressing into a clinical trial patient. Now, it's the challenge that we have in modern drug discovery and development is how do you transcend finding from clinical trials into real world patients?
And that's where we have the discipline of health economics and value assessment. come into play. For this reason, it is crucial for our models that are in the early stages of development to cross-talk with the models that are in play within market access. This is a fundamental premise of pharmacology to pair models. Our ultimate goal within drug discovery and development is to understand the real-world effectiveness of our therapies.
One component of which comes from clinical trial efficacy. And this is why there needs to be a robust translation between the patient in the clinical trial to a patient in the real world, which is the underlying premise, once again, for pharmacology to pair model. Let's reflect on some conclusions that we have learned in this module.
There are three... vital observations. The use of MIDD approaches are now so streamlined that it's no longer considered novel. It is an expected component of any drug discovery and development program. And when one reflects on the fact that there are more than 15 FDA guidance documents that include some form of modeling and simulation or MIDD as best practice, it is incumbent to realize that MIDD is a very integral component of drug development.
Second, a drug development program that does not include optimal considerations of modeling and simulation or MIDD is suboptimal. In other words, it could be a missed opportunity. It's critical to understand the likelihood that the compounds selected and vis-a-vis doses selected are safe and effective. It is important to understand the dynamic range of the molecule with which it engages with the target and the downstream pharmacology.
This not only helps identify the optimal dose range, but also eliminates the chances of using suboptimal doses. It's also important to understand the number of subjects required for trials. Are there unnecessary trials that could be reduced or eliminated?
MIDD helps answer all of those questions. And finally, MIDD is a proven component of the solution where information is optimized and is financially sustainable by removing uncertainty within drug development and regulatory pathways. It continues to evolve and its impact is only likely to grow. We would like to acknowledge the support and insights into these slides from the following individuals.
Dr. Craig Rayner of Sitara, Jeff Barrett of the Critical Path Institute, and Mark Selich, also of the Critical Path Institute. This presentation was brought to you by Critical Path Institute and Sitara. At Sitara. we accelerate medicines together.