Download Entry and Competition in the Brazilian Generic Drug Market

Entry and Competition in the Brazilian
Generic Drug Market∗
James Otterson †
IPEA-Rio
Eduardo P. S. Fiuza‡
IPEA-Rio and PUC-Rio
Dárcio G Pereira§
INPI
March 20, 2013
Abstract
This article studies entry and competition in the Brazilian pharmaceutical markets, following the European Commission’s Pharmaceutical Sector Inquiry released in 2009. A number of differences in the two
settings have to be dealt with. First, the Brazilian patent system is
quite novel, and very few drugs effectively changed their patent status
during the period studied (1999-2009). Second, Brazil has a homogeneous regulatory framework nationwide, as the major decisions of
patent examinations, marketing authorizations and price regulations
reside in Federal entities; this removes much of the regulatory variance encountered in the European Inquiry, which provides for more
We thank Mateus Gomes, Pedro Villardi, Gesiane Leão and Renan de Paula for their
valuable help. Dárcio Gomes and James Otterson work under the auspices of Ipea’s
Research Program for National Development (PNPD). We gratefully acknowledge the
Brazilian Ministry of Justice’s and PNPD’s financial support. The program Ipea-Pesquisa
benefited from the Inter-American Development Bank’s support that rendered viable an
access to Thomson Reuter’s Newport for Generics (TM). The opinions and views expressed
here are our own and do not represent an official view of our employers or anyone else that
we are affiliated with. Remaining errors are solely ours.
†
e-mail: [email protected]
‡
e-mail: eduardo.fi[email protected]
§
e-mail: [email protected]
∗
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precise estimators. On the other hand, unlike in Europe, Brazilian
regulation changed dramatically along the period studied. These differences determine some local adaptations for our estimation strategy.
The present version of this article reports estimates of time to entry
of unbranded generic versions of off-patent drugs into various therapeutic classes using hazard models with time-varying covariates. We
also combine count data models and binary regression in zero-inflated
count models for estimating the number of entrants within a postexpiration four-year frame. Results indicate that counts of patents by
type affect negatively the entry hazard rate. Price impact estimates
are pending for a next version.
Keywords: pharmaceutical industry, Brazil, generic drug, entry,
hazard model, zero-inflated count model, time-varying covariates
JEL Codes: L65, C25, L13, C41
1
Introduction
Generic drugs are an important market segment in the pharmaceutical industry and place a severe competitive constraint on Big Pharma’s market power
after patent expiration worldwide. Since the Hatch-Waxman Act was enacted
in the USA in 1984, generic drugs have proven to be an important tool for
enhancing competition and saving Governments’ and patients’ money across
the world. The recent Pharmaceutical Sector Inquiry [7] conducted by the
European Commission showed that the earlier the generic drugs get into the
markets, the faster the general prices drop. That study raised several issues
regarding regulatory failures and drug firms’ gaming strategies conducive to
entry deterrence, including sham litigation practices.
A replication of the Commission’s Inquiry (henceforth PSI) applied to
Brazilian data, however, requires a reasonable amount of adaptations. First
of all, in Brazil unbranded generic drugs were almost nonexistent until the
1990s. Drugs were authorized to be marketed without any test comparing
them to the pioneer (originator) drugs. Only in 1999, when the Generic Drug
Act was enacted, a formal link between the pioneer and the generic drug was
established. Unbranded generics were required to pass bioequivalence tests
and in exchange they not only were formally allowed to be dispensed by pharmacists in lieu of the originators, but also gained a massive advertisement
campaign by the Federal Government to convince prescribers and patients
2
of their status as “perfect substitutes”. A third class of drugs, called “similar
drugs” was then formally created, which could retain their own brands and
marketing efforts while being excluded both from bioequivalence tests and
from the official campaign. This situation was partially reversed in 2003,
when the similars were required by the 1999-established National Agency for
Health Surveillance (Anvisa, in its Portuguese acronym) to pass bioavailability tests on a rolling basis as soon as their respective marketing authorizations
expired and were to be renewed.
In addition, no patent for pharmaceuticals was enforced in Brazil before
the ratification of the TRIPs agreement by the Brazilian Congress in 1996.
The new Intellectual Property Act provided for a maximum of 20 years protection starting on the first priority date in the world, without any extensions.
On one hand, the first patents granted followed a fast track procedure that
merely convalidated the original patent without judgment on the merits – the
so-called pipeline inventions, with priority dates set in other countries prior
to the Act and not marketed in Brazil thus far. On the other hand, a new
patent application took on average four years to be examined and granted.
As a consequence, the first product patent expirations in Brazil occurred in
20011 , and the first non-pipeline patents expired in 2005.
The combination of these two features signifies that: a) generic drug
entries into the Brazilian market prior to March 2000 were restricted to
molecules which have never been subject to patent enforcement under Brazilian Law; b) due to the huge backlog and rather long processing time in the
Brazilian Patent and Trademark Office (INPI – National Institute of Industrial Property), many generic drug makers may have refrained from entering,
and incumbents may have enjoyed monopoly power for an excessively long
period, all the more so if we take into account that the grant rate at INPI is
about 8, 7%, much lower than in the USPTO (69, 5%) and in the European
Patent Office – EPO (76, 8%)2 ; c) most of the patents granted after formal
examination are still in force at the closure date of this article; d) many drugs
never had their primary patent filed in Brazil, and their originator companies
sustained their protection only through secondary patents; e) the number of
Before that, four composition patents expired in 2000. These are the earliest expiration
dates under the new legislation. Source: IMS Lifecycle.
2
Thes figures are based on preliminary estimates of the Brazilian Pharmaceutical Sector
Inquiry run by IPEA obtained in a search comprising patents from a sample of 197 drugs
filed in Brazil from May 15, 1997 to December 31, 1998, and comparing them to the same
patents in the two other jurisdictions.
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markets where generic drugs eventually entered is still minute (from 320 to
368 molecules, depending on the data source used).
Fiuza and Caballero (2010) ([10]) performed the first estimation of unbranded generic drug entry models in Brazil, using both count data and
ordered multinomial models. The preferred count model estimated was a
zero-inflated negative binomial (ZINB), and the data were segmented by
drug and year, in the period 1999-2007. The present estimations differ with
respect to the following features: i) the sample refers to a longer period (19992009); ii) the sample trades off a smaller number of molecules for a wider
variety of drug attributes to be used as explaining variables, which demanded
a substantial effort of data collection, especially of the patents related to the
drugs; iii) a different approach is followed, more closely related to the European PSI.
In fact, the PSI methodology had to be adapted to our present setting.
First, we estimate a hazard model with time-varying covariates, which nests
the Cox proportional hazard model, but not the Accelerated Failure Time or
the Weibull models used by the European Commission. Second, we combined
the equations for the probability of entry (logit/probit) and for the number of
entrants into a single model, namely the ZIP model, a variation of the ZINB
chosen by Fiuza and Caballero (2010), with one of the differences being that
the sample is simply a cross-section of the drugs, not a panel, so that the
estimated number of entrants is simply the number accumulated over the
sampled years after the Loss of Exclusivity (LoE) date.
The present article features six more sections. The next section summarizes the European Comission’s Pharmaceutical Sector Inquiry findings
whose occurrence in Brazil we intend to investigate herein. The section after
that brings a short description of the Brazilian pharmaceutical market and
some descriptive statistics. Section 4 surveys the literature on pharmaceutical entry. In the last three sections, we present modeling results and the
conclusion.
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2
The European Pharmaceutical Sector Inquiry:
highlights3
The European Commission’s Directorate General of Competition (DGComp)
may conduct sector inquiries, pursuant to Article 17 of the Council Regulation (EC) No 1/2003. That article states that the Commission "may decide
to conduct an inquiry into a particular sector of the economy or into particular types of agreements across various sectors, "where the trend of trade
between Member States, the rigidity of prices or other circumstances suggest
that competition may be restricted or distorted within the common market". On 15 January 2008 the European Commission launched an inquiry
into competition in the pharmaceuticals sector.
The inquiry was a response to indications that competition in Europe’s
pharmaceuticals markets were malfunctioning: lower entry rates for new
medicines, and delayed entry of generic medicines. In particular, the Inquiry
examined whether agreements between pharmaceutical companies, such as
settlements in patent disputes, have blocked or lead to delays in market entry.
For this purpose, the DGComp performed surprise inspections in pharmaceutical companies – not indicating that the Commission had any evidence
of wrongdoing, but to gain immediate acess to relevant information, which
by its nature are deemed highly confidential by the companies and might be
easily withheld, concealed or destroyed.
The final report confirmed the importance of patents for protecting the
innovators’ efforts. However, their effective protection time has been shortened and/or severely constrained by: increasing times for approval of new
drugs, severe controls on reimbursement status (incorporation into positive
lists and levels of co-payment) and increasing pressure from health plan sponsors to substitute generic drugs for originators. At the same time, the existing
methods for searching new molecules are facing increasing costs, and the increasing safety requirements of the regulatory agencies also entail greater and
more costly clinical trials.
It was verified that the originator drug companies resort to a wide array of
entry-deterrence strategies against generics. First, they file a great number of
Information on the Pharmaceutical Sector Inquiry was mainly compiled from the Final
Report and other official documents released when the Inquiry was launched and thereafter
during the investigations. All of them are gathered in the EU’s official webpage:
http://ec.europa.eu/competition/sectors/pharmaceuticals/inquiry/.
3
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secondary patents, thus forming the so-called patent clusters or thickets. This
creates litigation costs for generic companies and delays their entry. Some
of these litigations may be considered sham, in cases where the incumbents
know that their patents are weak and just want to gain time.
In many instances the disputes and litigations are settled, whereby the
originator companies are able to extract some advantages: entry may be
delayed, side payments and other side deals (e.g. distribution agreements
or cross-licenses in other geographic or product markets) may take place,
competition may be dampened. In the words of the Commission, agreements "designed to keep competitors out of the market may also run afoul
of EC competition law. Settlement agreements that limit generic entry and
include a value transfer from an originator company to one or more generic
companies are an example of such potentially anticompetitive agreements, in
particular where the motive of the agreement is the sharing of profits via
payments from originator to generic companies to the detriment of patients
and public health budgets." (PSI, p.524) The Commission has been monitoring such agreements since the launch of the final report, and the third and
last follow-up was released in July 2012 ([8]) and covering the calendar year
2011. Figures reported showed that settlements involving value transfers and
limiting generic entry had stabilized at a low level.
Second, originator drug companies may pursue evergreening strategies
that move doctors and patients to the so-called follow-on drugs (new formulations with extended release, new combinations etc., some of them object of
specific patents), thus also raising difficulties for generic companies to capture a share of the patients using those drugs, as these “new” drugs are not
perfectly substitutible by the existing generics.
Besides the institutional investigation and collection of documents on
firms’ strategies, the Commission gathered data to run econometric estimations. The estimations included: a time-to-entry equation, a regression on
the probability of entering, a count model for the number of entrants, an
equation of the impact of generic entry on average prices (long run price
drop regressions), and an equation for their market penetration. Combining
the results, the Commission was able to perform a counterfactual simulation
that suggested that consumers and health plan sponsors would save approximately 20-25% in costs to the health systems if generics entered right upon
the originators’ loss of exclusivity dates, instead of the average six months
delay reported.
It is worth noting that in the USA the Federal Trade Commission has also
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investigated the use of patent settlements for delaying generic entry. Under
Paragraph IV of the Patent Restoration Act, incumbents and first challenging
generic companies have incentives to sign side deals to delay entry. Benefits
exchanged moved from explicit side payments (which were prosecuted by the
FTC) to more disguised arrangements, such as distribution agreements or
incumbents refraining from launching own generics – see FTC (2011)[11]
3
The Brazilian market
Brazil was in 2010 the eighth largest market for pharmaceuticals in the
world4 , topped only by the USA, Japan, Germany, France, China, Italy
and Spain. From 2006 to 2010 sales increased on average 7.76% p.a. in real
terms, an astounding performance. Sales in units increased even more, at
an impressive 9.55% yearly rate (Source: Interfarma). The difference may be
due to the increasing penetration of generic drugs.
Indeed, the enactment of the Generic Drug Act in 1999 combined with a
massive avertising campaign helped boost the unbranded generic drug sales.
These drugs achieved in 2008 a 13.8 percent market share in value, 16.9 percent in volume (IMS Health apud Pro-Genérico). This is not a bad record if
one takes into account unbranded generic drugs’ infancy in the country, but
when we compare Brazilian figures with other selected countries with tradition in generic drugs, we conclude (see Table 1) that the generic-originator
price ratio is quite high, as the ratio of the revenue share to the volume share
is much higher than in the other countries.
Another indicator of the importance of unbranded generic drugs is the
present prominence of domestic drug companies in the market. The Big Four
Brazilian drug companies EMS, Medley, Eurofarma and Aché-Biosintética
accounted for approximately one quarter of the total sales in 2011. Their importance attracted the multinationals: Medley was bought by Sanofi-Aventis
in 2009, while Pfizer bought a 40% stake in another important generic firm,
Teuto, besides signing a distribution agreement with Eurofarma, both deals
taking place in 2010.
Source:
IMS
Market
Prognosis
2010-2014.
Available
http://www.interfarma.org.br/site2/images/M_images/ranking%20mundial.jpg
access on March 14, 2013).
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from
(last
Table 1: Generic Drug Market Share in Selected Countries
Country
% in Revenue % in Volume
USA
13
60
Germany
26
60
United Kingdom
26
60
Canada
22
45
France
14
35
Spain
13
30
Brazil
13.8
16.9
Source: Associação Brasileira das Indústrias de
Medicamentos Genéricos - Pró-Generico.
To better understand how the Brazilian market functions, a brief historical summary is useful. Price controls and absence of patent enforcement
were the main strategies of the military government since the beginning of
the 1970s. On the one hand, an import substitution attempt required that
foreign patents were not recognized by Brazilian law. In fact, chemicalpharmaceutical product patents were not enforced since 1945, and process
patents were also revoked in 1969. On the other hand, macroeconomic policy
in Brazil fostered inflation in the 1960s and 1970s, and this led authorities to
adopt price controls as countervailing measures. This regulatory framework
persisted until the early 1990s, when price controls as a whole were phased
out (pharmaceuticals in 1992) and replaced by a modern Antitrust legislation
(two consecutive Acts in 1993 and 1994). Import substitution mechanisms
were also removed (tariffs were lowered, and non-tariff barriers dismantled,
beginning in 1989), which entailed a drop in local content of drugs marketed
in Brazil. Pharmaceutical controls were reintroduced on the turn of the century, first using a very crude cost-plus rule of thumb and later (2003) moving
to an extended RPI - X price cap regime that remains in force at present.
A Pharmaceutical Interministerial Regulatory Board (CMED) was set up in
2000, who both controls annual price revisions and the introductory prices
for new drugs and packages.
In 1996 the Brazilian Congress ratified the Trade Related Intellectual
Property System (TRIPS) agreement (Lei 9279), and started enforcing patents
very early, in 1997 (for the sake of comparison, India, a country with a much
more powerful pharmaceutical industry, started enforcing patents in 2005
8
only). Brazil chose the lowest patent lifetime allowed by TRIPS, 20 years
(counted as from the first priority date), with no room for extensions, and in
1999 a second hurdle was erected to the patentees: pharmaceutical patents
are granted only after previous consent by Anvisa, the agency in charge of
drug marketing authorizations. On the other hand, a controversial provision
for pipeline inventions was introduced in the legislation (Section 230):
patents filed in Brazil from May 1996 to May 1997, and already filed in
at least one country, were eligible to require convalidation by the Brazilian
Patents’ Office, without national examination and even if the patent was
prior to TRIPS, as long as the product was not yet commercialized anywhere
or nobody had done efforts in the country for exploration at the time of the
request (Chaves & Reis [6]) – other products already marketed or whose
patents had already expired did not benefit from this. These patents were
both the first in and the first out: as they did not undergo a formal examination, they were quickly granted; on the other hand, as many of them benefited
from the maximum lag allowed by legislation, their exclusivity period after
granting was quite short. Moreover, the Brazilian INPI was not fully ready
to examine the great number of incoming patents filed, so the first pharmaceutical patents granted after a formal examination, and the examination
period increased gradually to a maximum of ten years.
The other major transformation in the 1990s was the enactment of the
first Generic Drug Act in 1999. As of that date, prescribing doctors had
their reasons to concern that drugs of a same API (Active Pharmaceutical
Ingredient) might not be substitutes for each other, because maketing authorizations required only safety tests and absolute effectiveness. By that
time, Brazil had only pioneer (not necessarily originator’s) and/or “similarťť
(branded or unbranded) versions of a same drug (API and formulation). The
Generic Drug Act (Lei 9787) introduced bioequivalence tests and the Government ’launched’ the ’genuine’ unbranded generic drugs advertising them
as bioequivalents, and therefore eligible for being dispensed by a pharmacist in lieu of their respective pioneers. Similar drugs were then mandated
to adopt brands if they were unbranded. Generic names (either Brazilian
or International Non-proprietary Names) have since then been allowed only
to drugs bioequivalent to the pioneer.5 On the other hand, branded drugs
According to Araujo et al (2010)[1], as soon as similar drug companies comply with
the Resolutions 133 and 134/2003, they shall be considered as bioequivalent to the pioneer
drug, but interchangeability would require a change in the generic drug legislation itself.
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may be pioneer (reference) or similar.6 Because of the TRIPS ratification,
both similar and generic drugs are now only authorized if the original drug
is off-patent (i.e. either the patent has already expired or it has never been
enforced). And it is worth noting that one of CMED’s criteria for introductory pricing is that unbranded generic drugs are priced at least 35% less than
their respective pioneer drugs, whereas similar drugs may be priced equal to
the pioneers.
Last, but note least, it is also worth noting that in Brazil the regulator
has to elect a new reference drug when the original one is discontinued, and
this may affect the bioequivalence status of existing generics.
A major concern (and source of complaints) for the generic drug companies is the excessively long time taken by Anvisa in processing the marketing
authorization applications.Soares etal (2012)[28] report that the average time
for drugs on ’fast-track’ was 429 days, while drugs in normal (non–prioritized)
applications took 626 days in a sample of applications filed from 2008 to 2010.
For the sake of comparison, other Latin American countries might take much
shorter times: 7 days in Peru, 1 month in Bolivia, 3 to 4 months in Argentina
and Colombia, while Brazil, Chile and Cuba would take 12 months in average. In the USA the time increased from 16,3 to 26,7 months, but one should
bear in mind that authorized generics may benefit from the same marketing
authorization, while in Brazil they have to undergo the analysis again7
4
Literature on pharmaceutical entry: a survey
To the best of our knowledge, only one article has estimated generic drug
entry in Brazil so far: Fiuza & Caballero (2010) [10] estimated a family of
count models for the number of generic entrants in Brazil and compared them
to ordered models. Most of the explaining variables utilized are significant,
6
Homedes, Linares and Ugalde [12] report that in most Latin American countries
(Bolivia, Chile, Colombia, Costa Rica, Ecuador, Nicaragua, Peru and Uruguay) pharmaceuticals are classified under two categories only: Branded (comprising branded originals,
branded generics and branded similar) versus Generics (original or own generics, copy
generics and unbranded similars). Argentina features three categories: innovative (branded
original or copy), similar (non-bioequivalent), while generic applies to bioequivalent drugs,
both branded and unbranded.
7
A Resolution from 2008 allowed these ’clone’ drugs to benefit from existing or pending
marketing authorizations. We have no data on the time to enter for this specific group of
drugs.
10
in particular potential market, market concentration, and age of the pioneer
drug. Other empirical papers on the Brazilian pharmaceutical industry basically focus on post-entry pricing – Fiuza and Caballero (2010)[10], Nishijima
(2008)[22], and Lopes (2009)[19].
As Reiffen and Ward [23] point out, the generic drug industry is a useful
field for studying empirically how competition evolves within a market: (i)
each chemical represents a distinct experiment; (ii) information about the
market for each drug is observable to researchers; e.g. date of opening of the
market (the patent expiration date); (iii) firms must sink significant costs
to apply for the authorities’ approval prior to knowing when, or how many,
rivals will enter the market.
The main empirical references in generic drug entry are Scott-Morton
(1999,2000) [25], [26] and Reiffen and Ward (2005) [23]. The three papers
studied the US market. The first paper estimates entry as a binary choice
for each potential entrant to submit an Abbreviated New Drug Applications
(ANDAs) to the FDA during the 1984-1994 period. She included both firm
and market characteristics in her model. The second article adopted a Poisson count data modelling for testing whether pre-expiration brand advertising
deters generic entry. Her sample covered drugs that lost patents from 1986
to 1991. The third article developed a system of equations to explain generic
entry, generic post-entry market share, and entry price simultaneously. The
authors used a data set covering 31 drugs that went off patent in the late
1980s and early 1990s in the USA.
Besides Scott-Morton (2000), also Moreno-Torres et al [21] and Iizuka
[14] apply count data models to explain the number of generic entrants respectively in Spain and Japan, two heavily regulated markets, with tight
price controls. The formers estimated a (zero-inflated) Fixed Effects Negative Binomial model in a 34-quarter-panel of 77 drugs. The latter estimated
a Negative Binomial model in pooled data of 97 drugs during 5 years. In
both cases, the lagged revenues, drug’s age, counts of competitors were significant in explaining entry with the expected signs. In addition, the Spanish
model included regulatory variables and the Japanese model included types
of prescribers.
Bae[2] used a proportional hazard regression model to estimate time of
delay (in days) to entry after patent expiration in the USA for a list of
77 single-API drugs that lost patent during the period 1987-1994. Estimates point out that entry is faster in markets of greater revenue, with fewer
branded competitors, and for chronic-use drugs. The Medicaid Drug Rebate
11
Program, which imposed mandatory rebates off the private sector wholesale
prices for the Medicaid (public) purchases, had a positive impact on the entry
delay.
Hudson[13] studied entry in four developed countries: USA, UK, Germany
and Japan. USA and Japan are the greatest pharmaceutical markets in
the world, as measured by revenues. He analyzed: (i) the determinants of
generic drug entry; (ii) determinants of entry delay when entry does occur;
(iii) impact of entry on the original brand’s sales. His results also suggest
that the greater the market, the more likely are entry and its impact on
the original drug’s sales. His estimations are based on a sample of the fifty
best-selling APIs present in at least one of these countries, and include OLS,
Probit and hazard models.
Finally, the Pharmaceutical Sector Inquiry (PSI) studied for the European Union (EU) Member States: (i) determinants of generic drug entry after
one or two years of the originator’s loss of exclusivity; (ii) determinants of
entry delay; (iii) determinants of the number of generic entrants; (iii) the
long-run (average) price drops following generic entry; (iv) long run generic
share following entry. Data used covered 25 Member States and 219 drugs
(INNs) whose loss of exclusivity occured during the period 2000-2007. For
(i) a Probit was run; for (ii) five different hazard model specifications were
tried: Cox, Weibull, Weibull with inverse normal frailty, Discrete with normal frailty, Discrete with non-parametric frailty; for (iii) a Negative Binomial
model was employed; for (iv) and (v) OLS and IV regressions were used.
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5.1
The models
Description of database and variables
In what follows we consider three different models measuring the probability
of generic drug entry, the number of generic drug maker entrants and the
time to a first generic drug entry. Our measures are at the molecule level;
that is, the markets studied are molecule markets. Our analysis is based on
a sample of the 140 top selling molecules of the Brazilian wholesale market.
We tracked the market characteristics of these drugs from 1999 to 2009 using
information from IMS market audits (Pharmaceutical Market Brazil - PMB),
ABC Farma (a monthly magazine containing official list prices), and patent
information from Thomson-Reuters Newport, the Orange Book, and from
12
INPI (the Brazilian Patent and Trademark Office). We excluded from our
analysis OTC and phytotherapic drug formulations.
As mentioned earlier, bioequivalent unbranded generic drugs were introduced by the Generic Drug Act, and the first generic drugs introduced were
made of APIs which had never been protected by patents in Brazil. Drugs
protected by patents had to wait for the patent expiration or loss of exclusivity 8 to have a generic version available from independent manufacturers
(while own or authorized generics could enter any time9 ). Therefore, it is
usual to take the date of the primary patent’s loss of exclusivity as the starting date from which the market is open to a generic drug formulation. In
our analysis we take the maximum of the date of loss of exclusivity of the
molecule patent, the date the branded market began, and January 1999, as
the first date when the market is open to generic penetration. We call this
date the market onset date.
Having established a market onset date, we proceed with an analysis of
the probability that there will be a generic penetration in the first four years
after market onset, an analysis of the number of entrants in the first four or
five years after market onset, and an analysis on the time it takes from the
date of market onset to the actual generic entry. To do so, we consider the
following set of explanatory variables (we refer to Section 6.1 for a statistical
description of these variables).
Variables pertaining to properties of the molecule or the molecule’s market in the year prior to market onset (excluding patent information).
• The log of revenue.
• The log of the average market price (and the square of this log).
• The number of laboratory groups10 operating on the branded sector.
Since the Brazilian Law does not provide for clinical data protection, nor for Supplementary Protection Certificates, patent expiration in Brazil is quite a synonim for loss of
exclusivity.
9
There is no legal impediment for the patent holder to market or outlicense a generic
version of its drugs prior to the loss of exclusivity. In our sample no such case has been
observed, but this may be due to imperfect information about licensing agreements. In
contrast, authorized generics are quite usual in the European market (see, for example,
[7]).
10
This is distinct from the number of laboratories. Several pharmaceutical laboratories
may belong to the same laboratory group. Groups were identified by means of a comprehensive survey of mergers and acquisitions in Brazil, combining data from the 2005
8
13
• The molecule’s age (the time from its first marketed formulation to the
date of market onset).
• A dummy variable indicating if the molecule is mainly used to treat a
chronic condition (a condition requiring a treatment longer than three
months).
The molecule’s patent information (we used the patent classifications provided by Thomson-Reuters Newport database).
• A dummy variable indicating whether the drug was protected by the
primary patent (measured at the market onset date).
• The number of incremental patents classified as Product, Process, General, Delivery, Use, Constraining and Component (time-varying stocks).
• A dummy variable indicating that the primary patent was in force
outside Brazil but had not been filed in Brazil (time-varying).
A molecule market is but a segment of a larger market. Relevant product markets for antitrust purposes are usually defined as the anatomicaltherapeutical class (ATC4). We control for properties of these larger markets
using the following variables.
• The molecule’s number of ATC4 markets.
• The average number of laboratories operationg on the ATC4 markets
of a given molecule.
• The average Herfindahl-Hirschman market concentration index (HHI)
of these ATC4 markets.
5.2
Probability of entry
In this section, we go over our analysis on the probability that there will be
a generic entrant in a molecule market in the first four years after market
onset. Overall, in the sample of 140 molecules we used for this analysis, 43
Grupemef’s M&A map, the Ministry of Justice, CADE (Antitrust Tribunal) and Abcfarma (a price list magazine). More details can be found in Fiuza and Caballero (2010)
[10].
14
molecules have a generic drug version (around 30%). Only 12 of them had a
generic version in the first two years after market onset; whereas 23 and 32
molecules had a generic version by the third and fourth years, respectively.
That is, roughly 74% of the observed first generic entries took place within
the four years after market onset. One also has to take into account censored
information. Since the database we used for this analysis ends in December
2009, we do not observe the market conditions of all sampled molecules in all
years after market onset. In particular, we lose track of 16 market molecules
by the fourth year since market onset. This represents 11% of the tracked
markets. This ratio increases to 15% and 28% by the fifth and eight years
after market onset, respectively. We focus our analysis on entries that took
place within the first four years of market onset since in this time frame we
can observe the characteristics of most markets with a generic entry without
loosing too much information due to censoring. We refer to Table 6.1 for a
summary of these statistics.
We run three different logit regressions to assess the factors that influence
the probability of generic market penetration. The results are presented in
Table 6.1. In these three regressions, we included overall market characteristics: the natural logarithms of revenue and price, the Herfindahl-Hirschman
index (HHI), the number of laboratory groups selling the molecule, and the
total number of years the molecule has being marketed (the molecule’s age).
We also control for the year of market onset with dummies for onset happening between 1999 to 2002, from 2003 to 2004, and from 2005 onwards. In
the three logit models that we consider, the log of revenue and of price and
the molecule’s age have a significant and positive impact on the probability
of generic entry, whereas HHI has a significant and negative impact on this
probability. In all other regression analysis we consider, the log of revenue
also has a positive and significant impact; higher log of revenues are associated with a higher number of entrants and shorter time to generic entry.
Only one of the three logit models considered indicates that the number of
laboratory groups have a significant and positive impact on the probability
of generic drug entry.
After controlling for the above market characteristics, we study the impact of incremental patents on generic entry. These patents do not protect
the molecule itself, but rather formulations, manufacturing processes, fixed
dosage combinations, etc. The present logit models measure the impacts of
counts of incremental patents by type (measured at the market onset year) on
the probability of generic entry. We test different combinations of incremen15
tal patents for robustness. We cannot include all of them together because of
high multicollinearity. In the first bundle of patents, we consider the Product,
Delivery and General incremental patent categories. The only case with a
significant estimate is the Product incremental patent type. It has a negative
effect on the probability of generic entry. Product incremental patents have
a significant negative impact in all regressions performed. That is, additional
Product patents tend to decrease the total number of generic entrants and
to postpone the entry of a first generic formulation. The second bundle of
incremental patents we consider consists of Constraining11 and Component
patent types. Of these, only the estimate of the Constraining patent type
is significant (negative). We also detect a significant and negative impact of
Constraining patents on the number of generic competitors. However, we do
not detect an impact of these types of patents on the time to entry. The final
bundle of patents consists of Use and Process incremental patent types. Use
incremental patent types are estimated to have a significant and negative
impact on the probability of a generic market penetration. Finally, in all of
three models we considered, we tested the impact of foreign primary patents.
These patents were not filed at the Brazilian Patent and Trademark Office
(INPI), but they are the primary patents protecting the drug and were filed
in the country of origin. In only one of the models this type of patent has a
significant (negative) impact on the probability of generic entry.
We ran a Wald test on the bundles of incremental patents. We cannot
reject the hypothesis that the first bundle has no effect of the probability
of generic entry; whereas this hypothesis is rejected for the other two bundles. We also ran a Stukel h-family logit test (as specified in [5], page 468)
to check if we should use the generalized h-family logit models instead of
the logit model. The test supports the null hypothesis that the logit is the
correct model specification. Using the AIC and BIC estimates, the second
model seems to be slightly better than the other models. We also ran a probit
regression using the same models and the AIC and BIC estimates are slightly
better in the probit models (by a point or less). The only two differences
between the probit and the logit estimates are: (1) the impact of Product
incremental patents is found negative with a p-value of .03 (the Wald test on
According to Thomson Reuters’ Newport (TM) glossary, Constraining patents "are
those that are most likely to prevent generic competition or may not be easily circumvented.Ť Newport’s customer support staff explained that this clasification is mainly based
on criteria such as the patent type, the originators’ claims, listing on the FDA Orange
Book, and requests for Supplementary Protection Certificates.
11
16
the first bundle of patents rejects the hypothesis that they have no impact
on generic entry at the .069 level); (2) the impact of foreign primary patents
became siginificant at the .1 level in the second model.
5.3
5.3.1
Number of generic entrants
Zero-Inflated Poisson model
We use a Zero-Inflated Poisson (ZIP) model to study the number of generic
entrants in a molecule market. A model in the same lines as presented here
is contained in [10]. Lambert [18] introduced the ZIP in order to account for
the excess number of zeros usually found in count regressions, which imply
overdispersion. If π is a Poisson density and b is a Bernoulli density, then
we try to model the number of generic entrants using the following density
function:
(
b(0) + b(1)π(0), if n = 0
F (n) =
b(1)π(n),
if n > 0.
The binary distribution is ordinarily specified as a logit with regressors zi
and parameters γ. The log-likelihood then becomes:
ln L (β) =
n
X
(1)
1 (yi = 0) ln (exp (z′i γ) + exp (− exp (x′i β))) +
i=1
n
X
(1 − 1 (yi =
0)) (yi x′i β
i=1
−
exp (− exp (x′i β)))
−
n
X
ln (1 + exp (z′i γ))
i=1
The zero-inflated negative binomial follows the same reasoning, just substituting the negative binomial for π .
5.3.2
Data and results
Overall, ZIP models estimates the number of entrants in two steps. First
the market faces a binary (0/1) entry hurdle and, in case this process selects
the chance to enter, a Poisson counting process starts out that defines the
number of entrants (which includes a zero). In the years we analyze (the first
four and five years after market onset), roughly 75% of the sample consists
of markets without generic penetration. This suggests that a ZIP model is
17
more appropriate to our circunstances than a Poisson model. Furthermore,
we carry out a Vuong test in all of our model specifications (large positive
values are indicative that the ZIP model is preferable to a Poisson one). The
test indicates that the ZIP model is better than the Poisson model in all
model specifications. We also test if a ZIP model is more suitable to our
analysis than a zero-inflated negative binomial model. This is true in all
model specifications we consider. We refer to Table 5 for the regressions’
results.
As in the probability of entry model of Section 5.2, the following market characteristics, measured at the year prior to market onset, are used as
regressors in the ZIP models: (1) the logarithms of revenue and price, (2)
the number of laboratory groups in the branded sector, (3) the HerfindahlHirschman Index (HHI) of the branded sector; (4) the molecule’s age. We
also control for the market onset year in all model specifications. The log of
revenue has a significant and positive impact on the number of generic entrants (the Poisson part of the model), and it tends to decrease the number of
zeros in the Bernoulli part of the model (in only one model specification the
estimated value is not significant). The log of price has a significant and negative impact of the number of zeros (higher prices are associated with higher
probability of entry), but it tends to be detrimental to a higher number of
generic entrants. The models considered detected no discernable influence of
the number of laboratory groups on the Bernoulli part of the model, but this
variable has positive and significant impact on the number of total generic
sellers. HHI offers a significant barrier to entry in all but one of the models;
whereas a moleculeŠs age has a positive and significant (in almost all models)
impact on entry.
We considered two different bundles of incremental patents measured at
the year of market onset. The first bundle consist of incremental patents
of Product, Delivery, and General types. The second bundle consist of incremental patents of Constraining and Component types. The models do
not detect an effect of incremental patents of Product and Delivery types on
the Bernoulli part of the model. However, incremental patents of Product
and General types have a (significant) negative effect on the total number
of generic entrants. This is the opposite effect estimated for incremental
patents of Delivery type. For the second bundle of incremental patents, only
incremental patents of Constraining type seem to have an impact on market
outcomes. They tend to decrease the total number of competitors selling
generic drugs and to decrease the probability of a first entrant. Primary
18
patents that were only filed in foreign countries are associated with a larger
number of entrants in the generic market, but seem to offer a hurdle to initial
market entrance.
5.4
Time to generic entry
We considered two main families of time to generic entry models in a molecule
market. In the first set of models, we ran three Cox regressions where all
regressors are time independent. All measurements were taken in the year
leading to market onset. The second set of models consider time varying
covariates. In particular, we track the impact incremental patents have on
the time to first generic entry allowing for changes in the number of these
patents over time. In all models below (Cox models with our without time
varying covariates), we ran a proportionality hazards test and did not reject
the assumption that the hazards are proportional (taking the covariates as a
group or individually). The models are presented in Table 6.2.1.
In all models reported, the log of revenue and the log of price have a
(significant) positive impact on the time to entry. (In the models using the
logarithm of price and its square, the estimated impact is positive for prices
above seven Reais. This is the case of more than 90% of the molecules in
regression’s sample.) Higher revenues and higher prices are associated with
a quicker first generic market entry. The revenue parameter estimate is in
line with the findings of the EU inquiry [7] and the regression results of the
previous two sections. The price coefficient estimates have the opposite sign
to the numbers obtained by the European PSI, and they are in line with our
previous probability to entry and number of entrants analysis of Sections 5.2
and 5.3, respectively. Notice that the results of our ZIP models we considered
tend to associate higher prices with an increase on generic entry but with a
decrease on the total number of competitors in the generic market.
In line with our previous models, the molecule’s age have a positive (significant) effect on the time to entry; whereas the Herfindahl-Hirschman index
(HHI) has a negative (significant) effect on that time. We also considered
market characteristics that we did not explore in the previous models. The
mean number of laboratory groups in the ATC4 markets of the molecule, and
the number of ATC4 classes of the molecule tend to have a negative impact
on the time to entry. The number of laboratories in the branded molecule
market prior to market onset has a positive (significant) impact on the time
to entry.
19
The first three models we consider only have time invariant covariates.
We control them for the year of market onset. Each model test the impact
of a different set of incremental patent bundles. They predict that Product,
Constraining and Process incremental patent types have a negative (significant) impact on the time to entry. They all predict that foreign primary
patents not filled in Brazil have a negative (significant) impact on the time
to first generic drug entry. Incremental patents of Delivery, General and Use
types do not seem to have an influence on the dependent variable.
The remaining models consider the number of incremental patent types
over time. We include in these models a dummy indicating the existence of a
previously expired Brazilian primary patent. The existence of such a patent
is associated with markets with quicker generic drug penetration. Incremental patents of Product, Delivery and Component types tend to extend time
to the first generic entry, as do foreign primary patents that were never deposited in Brazil. Incremental patents of Process, General and Constraining
types do not seem to affect the measured time to entry. Use incremental
patents type are associated with quicker first generic drug entries. This
may be because they are unsuccessfully trying to protect the drugs within
a broader evergreening strategy. In all regressions considered, we controlled
for the market onset year.
6
6.1
Model results
Description of the variables
20
Table 2: Descriptive statistics in the years after market onset
Years after
market
onset
G entrants
% of G
entrants
Without G
entry
Censored
cases
1
2
3
4
5
6
7
8
9
6
12
23
32
33
36
39
42
43
13
27
53
74
77
83
90
97
100
131
122
104
92
86
78
64
58
52
3
6
13
16
21
26
37
40
45
21
% of
censored
cases over
sampled
markets
2
4
9
11
15
18
26
28
32
Table 3: Descriptive statistics of the dependent variables in the year prior to market onset.
22
variable
Revenue in millions (2010
Reais)
Price (2010 Reais)
Number of Firms in
molecule market
Molecule’s age
Cronic
Number of ATC4 classes
Average number of Labs in
the molecule’s ATC4 markets
Average HHI of the
molecule’s ATC4 markets
Year of market onset
Patent Variables
Component
Contraining
Delivery
Formulation
General
Use
Process
Product
Foreign primary patent
Brazilian primary patent
N
120
mean
26.03393
sd
70.43487
p10
0.174147
p25
1.526506
p50
8.541561
p75
21.7205
p90
50.75023
min
0.000896
max
657.5314
sum
3124.072
120
128
271.7921
3.296875
924.5843
5.396644
13.1052
1
29.17199
1
65.56509
2
137.9767
3
389.262
6
1.79183
1
7822.181
32
32615.05
422
138
134
128
128
6.34058
0.574627
1.164063
16.84375
9.065452
0.496255
0.482405
16.23496
0
0
1
2
0
0
1
4
1
1
1
11.5
8
1
1
27
23
1
2
37
0
0
1
1
34
1
4
82
875
77
149
2156
128
0.424195
0.280584
0.15531
0.21907
0.31364
0.536063
0.947298
0.100974
1
54.29694
139
2000.971
2.951154
1999
1999
1999
2003
2007
1999
2009
278135
139
139
139
139
139
139
139
139
139
139
2.057554
0.510791
0.100719
1.985612
0.129496
1
0.805755
0.920863
0.438849
0.230216
3.702271
0.919727
0.422123
2.678377
0.493942
2.043016
1.718938
2.31274
0.498041
0.422493
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
3
1
0
3
0
1
1
1
1
0
6
2
0
6
0
3
3
2
1
1
0
0
0
0
0
0
0
0
0
0
21
5
3
17
4
12
10
22
1
1
286
71
14
276
18
139
112
128
61
32
Table 4: Logit model on the probability of generic penetration (margins at
mean)
ln Revenue
ln Price
HHI
N. Labs (mol. market)
age
Patent variables (at onset date)
Product
Delivery
General
dy/dx
0.098***
(0.024)
0.081**
(0.041)
-0.448***
(0.129)
0.011*
(0.017)
0.018***
(0.005)
dy/dx
0.090***
(0.019)
0.099***
(0.033)
-0.459***
(0.100)
0.017
(0.022)
0.016***
(0.004)
-0.084*
(0.046)
0.004
(0.045)
(0.147)
0.147
Constraining
-0.152***
(0.045)
-0.002
(0.007)
Component
Use
Process
Foreign Mol. Patent
dy/dx
0.095***
(0.017)
0.110***
(0.032)
-0.403***
(0.123)
0.008
(0.013)
0.016***
(0.004)
-0.032
(0.104)
-0.131
(0.080)
-0.033***
(0.010)
-0.011
(0.020)
-0.126*
(0.074)
(Omitted onset year)
Observations
96
96
96
Pseudo R-square
0.653
0.688
0.668
Stukel test
0.244
0.806
0.674
AIC
63.301
57.176
59.544
BIC
91.509
82.820
85.188
Wald test on patent vars.
0.129
0.025
0.029
Omitted dummies of grouped year of market onset
Tests and R-square estimates refer to the original logit regression, not the margins model
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
23
Table 5: ZIP model of the number of generic entrants
VARIABLES
ln Revenue
ln Price
N. Labs (mol. market)
Product
Delivery
Foreign Mol. Patent
General
First Four Years
Count
inflate
24
0.428***
(0.103)
-0.282**
(0.128)
0.035***
(0.009)
-0.238***
(0.063)
0.517***
(0.177)
0.580*
(0.346)
-0.417**
(0.182)
HHI
-2.087*
(1.254)
-3.728**
(1.899)
-0.063
(0.208)
2.173
(1.771)
-0.168
(0.609)
First Four Years
Count
inflate
0.545***
(0.105)
-0.172
(0.169)
0.023**
(0.009)
-0.239***
(0.089)
0.453**
(0.197)
0.958***
(0.271)
-0.444**
(0.201)
15.587**
(6.571)
-0.623***
(0.204)
age
-2.028
(1.897)
-2.667**
(1.072)
-0.257
(0.350)
3.169
(3.389)
0.455
(0.966)
Component
-5.955***
(1.706)
46.207**
(21.243)
-8.096***
(1.830)
40.166
(27.791)
First Four Years
Count
inflate
0.361***
(0.094)
-0.299
(0.242)
0.032***
(0.008)
-1.346**
(0.537)
-3.258**
(1.518)
-0.254
(0.508)
0.458***
(0.120)
-0.144
(0.165)
0.021**
(0.009)
-1.282***
(0.474)
-2.806***
(1.077)
-0.189
(0.413)
0.461
(0.417)
3.181
(3.780)
0.717***
(0.210)
3.445**
(1.587)
15.158
(10.177)
-0.596**
(0.252)
Constraining
Constant
First Four Years
Count
inflate
-0.697**
(0.272)
0.031
(0.050)
-4.655***
(1.529)
10.795***
(3.736)
-0.399
(0.255)
2.875**
(1.445)
32.336**
(13.047)
-0.725***
(0.195)
0.012
(0.027)
-6.560***
(2.262)
104
152.8
218.9
4.487
1
103
165.0
228.3
4.737
1
10.747***
(3.660)
-0.380*
(0.215)
-0.569
(1.020)
28.896**
(11.897)
(Omitted onset year)
Observations
AIC
BIC
Vuong
α = 0 test (ZIP 6= ZINB)
104
154.0
222.7
4.445
1
104
103
103
104
154.0
165.9
165.9
152.8
222.7
231.8
231.8
218.9
4.445
4.479
4.479
4.487
1
.08
.08
1
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
103
165.0
228.3
4.737
1
6.2
6.2.1
Time to generic entry
Description of key estimates
Figure 1: Time to generic entry.
Time to first generic entry
Smoothed hazard estimate
.03
0.00
.04
0.25
.05
0.50
.06
0.75
.07
1.00
Kaplan−Meier survival estimate
0
5
Time in years
10
0
2
4
Time in years
6
8
Figure 2: Time to generic entry by presence of foreign primary patent.
Time to first generic entry
Nelson−Aalen cumulative
0.80
hazard estimates
0.00
0.00
0.20
0.25
0.40
0.50
0.60
0.75
1.00
by foreign main molecule patent
Kaplan−Meier survival estimates
0
5
Time in years
Without
10
0
With
5
Time in years
Without
25
10
With
Figure 3: Time to generic entry restricted to the cases with entry.
Time to first generic entry
(cases with generic entry)
Smoothed hazard estimate
.3
0.00
.4
0.25
.5
0.50
.6
0.75
.7
.8
1.00
Kaplan−Meier survival estimate
0
2
4
Time in years
6
8
0
2
4
Time in years
6
8
Figure 4: Time to generic entry by laboratory market presence prior to the
onset date.
Time to first generic entry
Kaplan−Meier survival estimates
Nelson−Aalen cumulative
4.00
hazard estimates
0.00
0.00
1.00
0.25
2.00
0.50
3.00
0.75
1.00
by lab presence on the branded market on the onset date
0
2
4
Time in years
6
8
0
2
4
Time in years
6
Entrant not present
Entrant not present
Entrant present
Entrant present
Cases with generic entry
26
8
Table 6: Time to first generic entry
VARIABLES
ln Revenue
ln Price
ln Price2
N Labs ATC4 markets
N. Labs (mol. market)
HHI
Cronico
NClasse4
(1)
(2)
(3)
Time invariant regressors
1.037***
(0.279)
-2.672**
(1.324)
0.397**
(0.167)
-0.024
(0.018)
0.128**
(0.063)
-4.030**
(1.969)
-1.540***
(0.564)
-0.493
(0.455)
1.004***
(0.297)
-2.609*
(1.499)
0.392**
(0.192)
-0.020
(0.018)
0.134**
(0.062)
-4.644**
(2.069)
-1.367**
(0.562)
-0.457
(0.420)
0.909***
(0.262)
-2.400*
(1.403)
0.384**
(0.184)
-0.026
(0.018)
0.154**
(0.064)
-5.627**
(2.721)
-1.125**
(0.567)
-0.394
(0.426)
age
Product
-0.393*
(0.222)
Use
Delivery
-0.044
(0.125)
-0.745
(0.462)
Constraining
1.206***
(0.282)
1.950***
(0.446)
1.112***
(0.234)
2.107***
(0.486)
-0.071***
(0.019)
0.209***
(0.073)
-7.955***
(1.658)
-3.404***
(0.653)
-1.481***
(0.399)
0.184***
(0.030)
-0.226***
(0.070)
0.266**
(0.114)
-1.027*
(0.548)
-0.057***
(0.021)
0.186***
(0.061)
-7.340***
(1.384)
-2.959***
(0.670)
-1.183***
(0.353)
0.185***
(0.031)
-0.053***
(0.017)
0.196***
(0.070)
-8.102***
(1.718)
-2.639***
(0.613)
-1.165***
(0.401)
0.183***
(0.030)
-0.189
(0.376)
-0.058*
(0.030)
0.739
(0.607)
Process
1.524***
(0.580)
-2.408**
(1.095)
-0.387
(0.318)
-0.032
(0.026)
1.553***
(0.527)
-2.960***
(1.111)
612
197.3
259.1
612
199.0
260.8
-0.303*
(0.159)
Brazilian Mol. Patent
Foreign Mol. Patent
1.345***
(0.279)
1.890***
(0.438)
-0.416*
(0.226)
-0.088
(0.072)
Component
General
(4)
(5)
(6)
Time varying regressors
-1.233**
(0.615)
-1.403**
(0.551)
-1.735***
(0.627)
1.487***
(0.487)
-3.567**
(1.756)
(Omitted onset year)
Observations
AIC
BIC
111
111
111
612
240.8
229.5
230.4
192.0
295.0
264.7
265.6
258.3
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
27
7
Conclusions
This article estimates aspects of unbranded generic drug entry in Brazil using three types of models. The first model is a logit model estimating the
probability of generic drug entry, the second model is a zero-inflated count
(ZIP) model explaining the number of generic laboratory group entrants, and
the last model is a hazards model estimating the time to first generic drug
entry. Adapted version of the long run price drop and generic share equations
run by the EU Inquiry have not been authorized as yet to be published, as
they employ confidential microdata of actual prices practiced in the Brazilian
market, and the results have not been cleared by our project sponsor to be
divulgated. This obstacle may have been removed by the time of the EARIE
presentation, when a new version may be disclosed.
Logit model. From the logit regression analysis we conclude that the
molecule’s age and its revenue and price prior to the market onset date have a
positive (significant) effect on the probability to entry. In all other regression
analysis considered, the proxy for potential market (the lagged revenues of
the particular drug) has significant positive sign, as expected. The branded
sector market concentration prior to the generic market onset date has a negative (significant) impact on the measure probability, whereas the number
of firm groups in the molecule’s market tend to increase that probability.
If we consider the number of incremental patents, by patent type, prior to
market onset date, the model points out that incremental patents of Product, Constraining and Use types have a negative (significant) impact on the
probability of a generic drug entry. This signals that these kinds of patents
may be used for patent clustering, sham litigation and market evergreening strategies. Finaly, the models point out a tendency for foreign primary
patents (the first patents protecting the drug filed in the country of origin
but not in Brazil) to decrease generic drug’s entry probability.
ZIP model. The models reported estimate that revenue has a positive
(significant) impact on the total number of generic drug laboratory entrants.
They point out a tendency for revenue to increase the probability of the decission to entry. Price has the effect of having a positive (significant) impact on
the decission to enter the market, but a negative tendency on the count part
28
of the model. The number of laboratory groups on the branded sector prior
to the generic market onset date have a positive (significant) effect on the
count data part of the model. The drug’s age tends to have a positive (significant) effect on the decission towards market entry. Market concentration
prior to the generic market onset date tends to have a negative (significant)
impact on the decission towards market entry. Incremental patents of Product, Constraining and General types have a negative (significant) impact on
the count data part of the model. Foreign primary patents and Constraining patents tend to have a negative impact on the decission towards market
entry. Foreign primary patents and incremental patents of Delivery type
are associated with market environments with positive tendency towards a
higher number of generic drug firms.
Time to entry. The Kaplan-Meier estimator indicates that 25% of molecule
markets have a first generic drug entry after five years of the generic market
onset. A pairwise test using this crude and non-parametric estimator indicates that markets with foreign primary patents that were not filed in the
INPI (the Brazilian Patent and Trademark Office) have higher first generic
entry waiting times. This is in agreement with all of our Cox regression
results. These foreign patents have a negative (significant) impact on the
measured time to entry. If we restrict ourselves to markets that actually had
a generic drug entry, the median time to entry is four years (with more than
25% of entries occurring on the first six years after market onset). The fact
that the first generic drug was produced by a group that was already present
in the branded market sector in the year prior to the generic market onset
does not seem to influence the time to generic entry. Incremental patents
of Product, Delivery, Constraining, Component, and Process types tend to
have a negative impact on the hazard rate (and therefore a positive impact
on time to entry), whereas Use incremental patents seem to have the opposite
effect 12 . Revenue, Price, HHI, the molecule’s age, the number of firms on
the branded market prior to generic market onset date have the same sign
(and are significant) as in the other regression results. Drugs mainly used to
This result should not be surprising. In our sample we note that the Brazilian case
law has not been favorable to Use patents: they have been either denied by the INPI or
abandoned. Filing such patents may therefore be understood as a non credible bluff, as
generic drugs anticipate this outcome and enter at once.
12
29
treat chronic conditions, the number of ATC4 classes where the molecule is
present, and the number of firms operating in the markets of drugs sharing
the molecule’s ATC4 classes tend to have a negative impact on the time to
first generic entry.
This article is part of an ongoing series of studies on the pharmaceutical
industry in Brazil using data at the wholesale level. This broader project
includes: (i) the replication of the price drop and generic share equations
proposed by the European PSI, and the estimation of the welfare loss due to
delays in generic entry in Brazil; (ii) a model of entry using firm-level data
and sequential decision each year; thereby enabling us to add firm characteristics, as suggested by Kyle [15] and Scott-Morton [26], following Berry
[3]; in particular, we may use diversification measures, ingredient experience,
therapeutic class experience, form experience, and payroll information; (iii)
incorporating dynamics of drug entry, as modeled by Reiffen and Ward [23].
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