WO2019145980A1 - Generic drug adoption framework/tool (graf/graf-t) for differentiating and identifying high quality generic drugs - Google Patents

Abstract

The invention relates to a Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) comprising atleast one of the parameters of clinical effectiveness, safety, quality and cost to differentiate and identify high quality generics vis-à-vis substandard generic drugs. It further specifies a method to implement the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) for early adoption of a high quality generic drug and biosimilar, and commercial benefit for the inventor.

Description

“GENERIC DRUG ADOPTION FRAMEWORK/TOOL (GRAF/GRAF-T) FOR DIFFERENTIATING AND IDENTIFYING HIGH QUALITY

GENERIC DRUGS”

Technical filed:

The present invention relates to Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) and a method of implementing the same to differentiate and identify high quality generics vis-a-vis generic drugs without evidence of high quality.

Background and prior art:

Generic drugs including biosimilars are economical versions of the originator drugs that are comparable in dosage, form, strength, route of administration, quality, characteristics, and intended use. A biosimilar is a biological product that is highly similar to and has no clinically meaningful differences from an existing FDA- approved reference product. Multiple drugs are coming off patent, and several mainstay therapies are now produced as generics.

As the generic drugs enter the markets, one needs to be observant towards their quality, safety, and efficacy, particularly as the targeted therapies and complex therapeutic products come off patent. As per World Health Organization (WHO), ‘substandard’ drugs (also called ‘out of specification products’) are medical products (also called“out of specification”) authorized by national regulatory authorities, but fail to meet either national or international quality standards or specifications or in some cases, both. Poor-quality medicines can reach the market through substandard production due to inadequate quality-control processes during manufacture, which eventually affects the effectiveness and safety of the drug.

Doctors/health care providers, while ensuring patient care also have to give due consideration to the cost of treatment. In this context, as doctors prescribe a generic dmg, one should prioritize safety over efficacy and need to ensure that "primum non nocere", means "first, do no harm”.

US 8,386,274 Bl discloses systems and methods for a prescription safety network utilizing eligibility verification transactions.

US 8,374,797 B2 discloses system for improving antibiotic use in acute care hospitals.

But there still remains a need to provide a simplified framework that would help physicians and various stakeholders (pharmacists, payers) in differentiating a high- quality generic vis-a-vis a substandard generic drug.

Therefore, it is an objective of the present invention to provide a simplified framework that help physicians and various stakeholders in selecting high quality generic drugs over substandard generic drugs based on the parameters such as clinical effectiveness, safety, quality and manufacturing diligence.

Summary of the invention:

In accordance with the above object, the present invention provides Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF- T) from Dr. Reddy’s Laboratories Ltd that comprises various parameters as clinical effectiveness, safety, quality and manufacturing diligence that help differentiating a high quality generic drug and biosimilar from a substandard one for early adoption of a high quality generic drug and biosimilar, and commercial benefit for the inventor and a method to implement the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T)

Brief description of the figures

Figure 1 Illustrates GRAF -High level Generic process flow. Detailed description of the invention:

The invention will now be described in detail in connection with certain preferred and optional embodiments, so that various aspects thereof may be fully understood and appreciated and the desired goal be accomplished.

The GRAF/GRAF-T framework according to the present invention is designed to be an aid for a rational approach towards generic adoption by Health Care Providers, Pharmacists and Payers. It accomplishes the goals by leveraging data retrieved by propagating questionnaire to the aforementioned participants.

Accordingly, in an embodiment, the present invention provides Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF- T) that comprises various parameters as clinical effectiveness, safety, quality, cost, and manufacturing diligence.

In order to achieve the purpose of this invention, collaborating with health care providers/pharmacists/payers/end users are essential to arrive at an opinion to assess the safety and efficacy of the generic drugs in addition to the cost and quality and developing the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) constituting of various parameters which helps in differentiating a high-quality generic drug.

In another embodiment, the invention provides a method of differentiating high quality generics vis-a-vis a substandard generic drug, which method comprises; a) Determining the questions for the assessment of clinical efficacy, safety, quality and cost of the generic drug;

b) Discussing the framed questions with various healthcare providers, pharmacists and payers at various countries;

c) Validation and finalization of the questions followed by developing the Generic Drug Adoption Framework tool/ Generic Drug Adoption Framework; d) Framework to consist of questions with graded responses so that all the drugs from companies can be compared on all pillars of decision making; e) Publication/presentation of the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework tool (GRAF-T) at various country forums;

f) Preparing the Generic Drug Adoption Framework tool (GRAF-T)/ Generic Drug Adoption Framework

g) Translating and printing the tool kit (printing material) and rolling out in the countries.

h) Utilisation of GRAF/ GRAF-T by HCPs and procurement agencies t ensure only high quality generic drugs are available in country of interest.

In a preferred embodiment, the Generic Drug Adoption Framework will be arrived at based on the safety and efficacy of the generic drug.

In another preferred embodiment, the Generic Drug Adoption Framework will be arrived at based on the cost and quality of the generic drug.

In another preferred embodiment, the Generic Drug Adoption Framework tool may be country specific or Region, as the results obtained are dependent on genetic constitution of the subjects and demography of the region where the subjects resides.

In yet another preferred embodiment, the invention provides an action plan for various countries as to the method of implementation of the Generic Drug Adoption Framework, which method comprises;

a) discussing with societies/Lead Key Opinion Leaders (KOLs)/lead physicians for partnering for GRAF/GRAF-T;

b) planning of focus group discussion (FGD) with blocks of 15-20 Doctors and Pharmacists (separate groups) at country level; c) obtaining the data to be analysed to prepare the country specific GRAF/GRAF-T guidance document;

d) Identifying local conference to present the country specific GRAF/GRAF- T publication;

e) Presenting the GRAF/GRAF-T data followed by publishing the data;

f) Rolling out in the country with GRAF tool Kit. The tool kit can be a hard copy, a computer based program, mobile Application, or a simple excel based calculator.

g) Framework to have series of questions where responses are graded and an aggregate score for a particular product from particular company to be obtained.

h) Products from company to be ranked as per aggregate score and top ranking products to be called high quality products.

The GRAF tool kit essentially comprises the data about safety, efficacy, quality, manufacturing diligence and cost of the generic drugs in the order to identify the most safe, effective and high-quality generic drug to be prescribed and administered to the subjects in need thereof.

In another preferred embodiment, the GRAF tool kit may be provided in computer readable medium to the physicians for ease of understanding the GRAF-T/GRAF and to prescribe the high quality generic drug accordingly.

The GRAF Kit according to the present invention provides guidance on selecting high-quality generic basically and enables Health care providers/Pharmacists/Payers to shift to/use a high-quality generic in that specific country, where the Generic Drug Adoption Framework of the present invention has been implemented.

The present inventors envisage that the Generic Drug Adoption Framework as illustrated in Figure 1 of the present invention to be an aid for a rational approach towards generic adoption by Health care providers, pharmacist and payers through different Channels of propagation. The description that follows would further throw light to illuminate the path towards accomplishing the desired rational approach.

Channels of propagation include but not limited to Paper based surveys, Web based application or Websites, Mobile applications (Android, iOS, Symbian, Windows mobile) as well as Oral Surveys and the like. The data is collected, not limited to, on four pillars of Efficacy, Safety, Quality and Cost along with related data like Demographic information of the participants.

The data is then processed into the digital mode and stored in secure application servers compliant with the regulations of the country, following which it is processed to measure the impact of the perception and knowledge of the participants regarding the safety and quality of the generic drugs as implied in the GRAF/GRAF-T framework.

The data is processed according to the following Classification, Migration, Association and Prediction (CMAP) alleyway.

Classification: The process starts by collecting the response data and then using a set of pre-decided metrics to sort them in various pillars demonstrating perception and need for more involvement by presenting data on drug or company. There are distinct boundaries between the pillars, so any movement among them can be measured effectively.

Migration: In this process, the surveys are again conducted after a certain amount of time and then the movements are measured as samples move between different pillars. The migration data is placed in a migration matrix and the movement is quantified based on the metrics identified during Classification step The data allows to review the effectiveness of the GRAF/GRAF-T implementation to increase awareness about the quality and efficacy of generic drug among the Healthcare Professionals, Pharmacists and Payers, and select a high-quality generic. Association: Next the processed data is used to create rules that would explain the behaviour of the variables.

A rule is an implication A C.

A rule makes sense thanks to its supports’ and confidence‘C’.

Support is an indication of how frequently the itemset appears in the dataset.

The support of A with respect to C is defined as the proportion of transactions in the dataset which contains the items.

Confidence‘C’

Confidence is an indication of how often the rale has been found to be true.

The confidence value of a rule, with respect to a set of transactions is the proportion of the transactions that contains A which also contains C.

The rules are created using various confidence and support measures basis various algorithms like Apriori, FP-Growth or others and the like.

Apriori:

Each transaction is seen as a set of items (an itemset }. Given a threshold C, the Apriori algorithm identifies the item sets winch are subsets of at least C transactions in the database.

Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation ), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found

FP Growth:

FP stands for frequent pattern.

In the first pass, the algorithm counts occurrence of items (attribute-value pairs) in the dataset, and stores them to 'header table'. In the second pass, it builds the FP- tree structure by inserting instances. Items in each instance have to be sorted by descending order of their frequency in the dataset, so that the tree can be processed quickly. Items in each instance that do not meet minimum coverage threshold are discarded. If many instances share most frequent items, FP-tree provides high compression close to tree root.

Recursive processing of this compressed version of main dataset grows large item sets directly, instead of generating candidate items and testing them against the entire database. Growth starts from the bottom of the header table (having longest branches), by finding all instances matching given condition. New tree is created, with counts projected from the original tree corresponding to the set of instances that are conditional on the attribute, with each node getting sum of its children counts. Recursive growth ends when no individual items conditional on the attribute meet minimum support threshold, and processing continues on the remaining header items of the original FP-tree

Other Association rules

Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) are association rules where each item may have several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations live in, nearby and humid.“Those who live in a place which is nearby a city with humid climate type and also are younger than 20 -> their health condition is good”. Such association rules are extractable from RDBMS (Relational database management system) data or semantic web data.

Context Based Association Rules are a form of association rule.

Context Based Association Rules claims more accuracy in association rule mining by considering a hidden variable named context variable which changes the final set of association rules depending upon the value of context variables. For example, the baskets orientation in market basket analysis reflects an odd pattern in the early days of month. This might be because of abnormal context i.e. salary is drawn at the start of the month. Contrast set learning is a form of associative learning. Contrast set learners use rules that differ meaningfully in their distribution across subsets.

Weighted class learning is another form of associative learning in which weight may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results.

High-order pattern discovery facilitate the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real-world data.

K-optimal pattern discoyery provides an alternative to the standard approach to association rule learning that requires that each pattern appear frequently in the data.

Approximate Frequent Itemset mining is a relaxed version of Frequent Itemset mining that allows some of the items in some of the rows to he 0.

Generalized Association Rules hierarchical taxonomy (concept hierarchy)

Quantitative Association Rules categorical and quantitative data

Prediction: Once the rules have been generated they are used to predict the behaviour of the variables in the future. It allows us to understand the impact of the current practices under the GRAF/GRAF-T framework. Data is processed by a rules engine which gets generated during the step of association, and probable outcomes of the decisions are generated by the system.

The following examples which include preferred embodiments will serve to illustrate the practice of this invention, it being understood that the particulars shown by way of examples are for purpose of illustrative discussion of preferred embodiments of the invention and are not limiting the scope of the invention. Example 1:

The questions determined for the assessment of efficacy of the generic drug:

1. Do generic drugs represent pharmaceutical products that are identical to innovator in chemical and pharmaceutical forms?

2. Do you consider bio-equivalence/pharmaceutical equivalence reports of generic medicines a good surrogate of effectiveness being equal to innovator?

3. Do you believe that effect (efficacy) of the generic drug is similar to the branded drug

4. Would prescribing generics (if lesser priced) improve patient compliance in patients who pay for medicines from their pocket

5. Your personal experience (clinical study) with a new generic drug is required for your regular adoption of generics in clinical practice?

6. Would you consider experience sharing of using generics by peers in other countries as a positive influencer to your confidence in generics?

Example 2

The questions determined for the assessment of safety of the generic drug:

1. Do you experience the side effect profile of a generic drug being similar to the originator?

2. Having a good Pharmacovigilance system by a generics company in regulated markets (USA/EU) builds your confidence on a generic product

3. Having a Pharmacovigilance system by a generics company in your country builds your confidence in a generic product

4. If the company supplies generic product to USA/ EU, it enhances your confidence on safety and quality of that generic drug company

5. Do you consider that the generics produced by different companies can vary in safety, efficacy, and quality

6. Having structured guidance (Like GRAF/GRAF-T) on defining safe and high quality generics will help you adopt /increase usage of a generic drug Example 3

The questions determined for the assessment of quality of the generic drug:

1. A generic drug approved in highly regulated country will increase your acceptance of that product

2. Information on stability and sterility would be helpful to consider if generic drug has high quality

3. Consistent availability of same generic brand for complete treatment cycle is an important factor for considering usage of particular brand

4. Would you like to see quality certificates (eg. Plant GMP, certificate of analysis, etc) of generic drug to get assurance of quality?

5. Would visiting to a generic manufacturing facility help you build confidence in a generic drug or company?

6. Information on generic company from independent third party sources like News and Media influences your decision to prescribe a brand?

Example 4

The questions determined for the assessment of cost of the generic drug:

1. Are you generally aware of the price of drug and cost of total therapy while prescribing to patients?

2. Is cost of drug an important variable in your mind before prescription?

3. Is the profit margin for clinic/hospital an important factor for you to choose a brand?

4. Do you consider“generic switching” mid-treatment cycle to a high-quality generic drug to reduce treatment cost for patients?

5. Should physicians replace innovator products with high-quality generic drug to reduce healthcare cost for hospitals or country?

6. Are company sponsored patient assistance programs (PAP) important factor for choosing an innovator or a generic drug? Example 5:

Developing the Generic Drug Adoption Framework Tool (GRAF-T)/ Generic Drug Adoption Framework (GRAF)

The questions as defined under the examples 1 to 4 would be validated based on the answers obtained to the questions and the questions will be finalized and further the Generic Drug Adoption Framework tool is developed after corroborating and compilation of the answers or the data obtained through various sources such as pharmacists, physicians, payers and end users. The data obtained through various sources is processed according to the CMAP alleyway as described hereinbefore.

Example 6:

Rolling out the GRAF-T/GRAF in a country

The compiled data thus obtained from the example 5 is thereby prepared for publication and presentation at various forums and before the appropriate authorities as Generic Drug Adoption Framework (GRAF) / Generic Drug Adoption Framework Tool (GRAF-T). This Generic Drug Adoption Framework is further translated and printed as tool kit for rolling out in the country.

Claims

We claim;

1. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) adopting two or more of the following parameters selected from the group consisting of

a. clinical effectiveness,

b. safety

c. Manufacturing diligence or quality and

d. cost minimisation

to differentiate a high quality generic drug and biosimilar from a substandard one.

2. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1 arrived on the basis of the safety and efficacy of the generic drug.

3. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1 arrived on the basis of the cost and quality of the generic drug and the company that manufactures it.

4. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1 wherein the framework/ tool is country specific.

5. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1 wherein the GRAF tool kit (printing material) is provided in computer readable medium to the physicians for ease of understanding the GRAF-T/GRAF and to prescribe the high quality generic drug.

6. A Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1 wherein the tool provides guidance on selecting high-quality generic basically and enables Health care providers/Pharmacists/Payers through different Channels of propagation to shift to/use a high-quality generic in that specific country of implementation.

7. A method to implement Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 1.

8. A method to implement Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 8 comprising the following steps;

a. Determining the questions for the assessment of clinical efficacy, safety, quality and cost of the generic drug;

b. Discussing the framed questions with various healthcare providers, pharmacists and payers at various countries;

c. Validation and finalization of the questions followed by developing the Generic Drug Adoption Framework tool/ Generic Drug Adoption Framework;

d. Publication/presentation of the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework tool (GRAF-T) at various country forums;

e. Preparing the Generic Drug Adoption Framework tool (GRAF-T)/ Generic Drug Adoption Framework

f. Translating and printing the tool kit and rolling out in the countries.

9. A method to implement Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 7 wherein it provides an action plan for implementation of the Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) in various countries.

10. A method to implement Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in claim 7 and 9 comprising the following steps for implementation of the action plan a) discussing with societies/Lead Key Opinion Leaders (KOLs)/lead physicians for partnering for GRAF/GRAF-T;

b) planning of focus group discussion (FGD) with blocks of 15-20 Doctors and Pharmacists (separate groups) at country level;

c) obtaining the data to be analysed to prepare the country specific GRAF/GRAF-T guidance document;

d) Identifying local conference to present the country specific GRAF/GRAF- T publication;

e) Presenting the GRAF/GRAF-T data followed by publishing the data; f) Rolling out in the country with GRAF tool Kit.

11. A method to implement Generic Drug Adoption Framework (GRAF)/ Generic Drug Adoption Framework Tool (GRAF-T) as claimed in 10 wherein the data is processed following Classification, Migration, Association and Prediction (CMAP) alleyway.